
En este proyecto vamos a analizar los datos aportados por Medicos Sin Fronteras y preparar un modelo para la predicción de aumento de cuotas de socios.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', 1200)
pd.set_option('display.max_rows', 1200)
pd.set_option('max_colwidt', None)
# Importamos dataset de CONTACTOS
df_contactos = pd.read_parquet("MSF_Contact.parquet")
df_contactos.head()
| id | msf_seniority__c | npo02__best_gift_year__c | msf_birthyear__c | msf_entrycampaign__c | msf_firstcampaignrecurringdonorchannel__c | leadsource | msf_firstcampaigncolaborationchannel__c | msf_returnedmail__c | npo02__averageamount__c | msf_isactivedonor__c | msf_isactiverecurringdonor__c | npsp__deceased__c | msf_begindatemsf__c | msf_fechacambiolevelrelacion__c | msf_datefirstdonation__c | msf_datefirstrecurringdonorquota__c | msf_datelastrecurringdonorquota__c | msf_datelastdonation__c | npsp__largest_soft_credit_date__c | npsp__first_soft_credit_date__c | msf_entrydatecurrentrecurringdonor__c | npsp__last_soft_credit_date__c | msf_firstentrydaterecurringdonor__c | npo02__firstclosedate__c | msf_lastrecurringdonationdate__c | npo02__lastclosedate__c | msf_lastdonationunique__c | gender__c | msf_crmexternalid__c | msf_languagepreferer__c | npo02__largestamount__c | npo02__smallestamount__c | npsp__first_soft_credit_amount__c | npo02__lastoppamount__c | npsp__last_soft_credit_amount__c | msf_annualizedquotachange__c | msf_relationshiplevel__c | msf_ltvcont__c | msf_ltvdesc__c | msf_ltvscore__c | mailingstate | npsp__largest_soft_credit_amount__c | msf_contactdeletereason__c | npo02__soft_credit_last_year__c | npo02__soft_credit_this_year__c | npo02__soft_credit_two_years_ago__c | msf_noagradecimientosmi__c | msf_noagradecimientoscp__c | msf_noagradecimientosemail__c | msf_noagradecimientossms__c | msf_noagradecimientostelefono__c | msf_nocaptacionfondoscp__c | msf_nocaptacionfondosemail__c | msf_nocaptacionfondosmi__c | msf_nocaptacionfondossms__c | msf_nocartasplanrelacioncp__c | msf_nocertificadofiscalcp__c | msf_nocertificadofiscalemail__c | msf_nocertificadofiscalmi__c | msf_nocertificadofiscalsms__c | msf_nocesionimagenpromocion__c | msf_nocomunicacionesonetooneemail__c | msf_nocomunicaconesonetoonemi__c | msf_nocomunicacionesonetoonecp__c | msf_nocomunicaconesonetoonesms__c | msf_nocomunicacionesonetoonetelefono__c | msf_noemailingstematicosemail__c | msf_noencuestaestudioconcursoemail__c | msf_noencuestaestudioconcursomi__c | msf_nollamadasbienvenidasencuestasotras__c | msf_noencuestaestudioconcursosms__c | msf_noencuestaestudioconcursotelefono__c | msf_noinformaciontestamentaria__c | msf_noinvitacioneseventosmi__c | msf_noinvitacioneseventoscp__c | msf_noinvitacioneseventosemail__c | msf_noinvitacioneseventossms__c | msf_noinvitacioneseventostelefono__c | msf_nomailingstematicoscp__c | msf_nomemoriacp__c | msf_nomemoriaemail__c | msf_nomemoriami__c | msf_nomemoriasms__c | msf_nomensajesplanrelacionsms__c | msf_nomensajestematicosmi__c | msf_nomensajestematicossms__c | msf_nonewsletteremail__c | msf_noplanrelacionemail__c | msf_nomensajesplanrelacionmi__c | msf_noplanrelaciontelefono__c | msf_norevistacp__c | msf_norevistaemail__c | msf_norevistami__c | msf_norevistasms__c | msf_notelemarketingcaptacionfondos__c | msf_hasfirstdonation__c | msf_hasfirstnewrecurringdonation__c | msf_firstcampaignentryrecurringdonor__c | msf_firstcampaingcolaboration__c | msf_firstannualizedquota__c | msf_program__c | msf_programaherencias__c | msf_programais__c | msf_pressurecomplaint__c | msf_recencydonorcont__c | msf_recencydonordesc__c | msf_recencyrecurringdonorcont__c | msf_recencyrecurringdonordesc__c | msf_recencytotalcont__c | msf_recencytotalscore__c | recordtypeid | msf_contactinformationsummary__c | msf_percomssummary__c | msf_rfvdonor__c | msf_rfvrecurringdonor__c | title | msf_scoringrfvdonor__c | msf_scoringrfvrecurringdonor__c | msf_scoringrvtotal__c | msf_mailingsegment__c | msf_legacyconfidentiality__c | msf_membertype__c | npo02__totaloppamount__c | npo02__oppamountthisyear__c | npo02__oppamount2yearsago__c | npo02__oppamountlastyear__c | npo02__best_gift_year_total__c | msf_totalfiscaloppamount__c | msf_lastannualizedquota__c | msf_valuetotalcont__c | msf_valuetotaldesc__c | msf_valuedonorcont__c | msf_valuedonordesc__c | msf_lastyeardonorvalue__c | msf_maximumdonorvalue__c | msf_averagedonorvalue__c | msf_lifetime__c | msf_commitment__c | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0033Y00002uqxIJQAY | 19.0 | 2005 | 7013Y000001mrCzQAI | Otro | Otro | False | 0.0 | Exdonante | Nunca | False | 2005-02-28 | 2020-03-28 | 2005-01-07 | None | None | 2005-01-07 | None | None | None | None | None | 2005-01-07 | None | None | None | Male | 10165165 | ESP | 0.0 | 0.0 | NaN | 50.0 | NaN | NaN | a0l0O00000k727RQAQ | 50.0 | Muy bajo 50 - 100 | 2.0 | PONTEVEDRA | NaN | NaN | NaN | NaN | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | 7013Y000001mrCzQAI | NaN | Reactivación/conversión EXDonantes MASS | False | False | False | 6756.0 | +10años | NaN | Nunca | 6756.0 | 1.0 | 0120O000000LBoCQAW | Sólo correo | Todo | 112 | 000 | 1.5 | 0.0 | 1.8 | DON MUY ANTIGUOS | False | Exdonante | 50.0 | 0.0 | 0.0 | 0.0 | 50.0 | 50.0 | NaN | 50.0 | Muy bajo | 50.0 | Muy bajo | NaN | 50.0 | 50.0 | 0.0 | 0.0 | |||||
| 1 | 0033Y00002uqxIRQAY | 19.0 | 2005 | 7013Y000001mrCzQAI | Otro | Otro | False | 0.0 | Exdonante | Nunca | False | 2005-02-28 | 2020-03-28 | 2005-01-07 | None | None | 2005-01-07 | None | None | None | None | None | 2005-01-07 | None | None | None | Female | 10165173 | ESP | 0.0 | 0.0 | NaN | 30.0 | NaN | NaN | a0l0O00000k727RQAQ | 30.0 | Muy bajo 0,10 - 50 | 1.0 | MADRID | NaN | NaN | NaN | NaN | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | 7013Y000001mrCzQAI | NaN | Reactivación/conversión EXDonantes MASS | False | False | False | 6756.0 | +10años | NaN | Nunca | 6756.0 | 1.0 | 0120O000000LBoCQAW | Sólo correo | Todo | 111 | 000 | 1.0 | 0.0 | 1.0 | DON MUY ANTIGUOS | False | Exdonante | 30.0 | 0.0 | 0.0 | 0.0 | 30.0 | 30.0 | NaN | 30.0 | Muy bajo | 30.0 | Muy bajo | NaN | 30.0 | 30.0 | 0.0 | 0.0 | |||||
| 2 | 0033Y00002uqxIZQAY | 19.0 | 2005 | 7013Y000001mrCzQAI | Otro | Otro | False | 0.0 | Exdonante | Nunca | False | 2005-02-28 | 2020-03-28 | 2005-01-05 | None | None | 2009-11-20 | None | None | None | None | None | 2005-01-05 | None | None | None | Other | 10165185 | ESP | 0.0 | 0.0 | NaN | 70.0 | NaN | NaN | a0l0O00000k727RQAQ | 3320.0 | Muy Alto 3.000 - 10.000 | 8.0 | SEVILLA | NaN | NaN | NaN | NaN | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | 7013Y000001mrCzQAI | NaN | Empresas y Colectivos Mass | False | False | False | 4978.0 | +10años | NaN | Nunca | 4978.0 | 1.0 | 0120O000000LBoDQAW | Teléfono+Correo | Todo | 125 | 000 | 3.3 | 0.0 | 4.2 | EMPRESAS NO SOCIAS | False | Exdonante | 3320.0 | 0.0 | 0.0 | 0.0 | 3050.0 | 3320.0 | NaN | 270.0 | Medio | 270.0 | Medio | NaN | 3000.0 | 830.0 | 4.0 | 0.0 | |||||
| 3 | 0033Y00002uqxIhQAI | 17.0 | 2010 | 7013Y000001mrCzQAI | Otro | Otro | False | 0.0 | Exdonante | Nunca | False | 2005-02-28 | 2020-03-28 | 2007-01-03 | None | None | 2012-01-20 | None | None | None | None | None | 2007-01-03 | None | None | None | Male | 10165201 | CAT | 0.0 | 0.0 | NaN | 120.0 | NaN | NaN | a0l0O00000k727RQAQ | 720.0 | Alto 500 - 1.000 | 6.0 | LLEIDA | NaN | NaN | NaN | NaN | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | 7013Y000001mrCzQAI | NaN | Reactivación/conversión EXDonantes MASS | False | False | False | 4187.0 | +10años | NaN | Nunca | 4187.0 | 1.0 | 0120O000000LBoCQAW | Sólo correo | Todo | 114 | 000 | 2.5 | 0.0 | 3.4 | DON MUY ANTIGUOS | False | Exdonante | 720.0 | 0.0 | 0.0 | 0.0 | 240.0 | 720.0 | NaN | 120.0 | Bajo | 120.0 | Bajo | NaN | 120.0 | 120.0 | 5.0 | 1.0 | |||||
| 4 | 0033Y00002uqxIpQAI | 18.0 | 2005 | 7013Y000001mrFFQAY | Otro | False | 0.0 | Exdonante | Nunca | False | 2005-03-01 | 2020-03-28 | 2005-03-01 | None | None | 2005-03-01 | None | None | None | None | None | 2005-03-01 | None | None | None | Other | 10165216 | ESP | 0.0 | 0.0 | NaN | 100.0 | NaN | NaN | a0l0O00000k727RQAQ | 100.0 | Muy bajo 100 - 120 | 3.0 | NaN | NaN | NaN | NaN | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | 7013Y000001mrFFQAY | NaN | Reactivación/conversión EXDonantes MASS | False | False | False | 6703.0 | +10años | NaN | Nunca | 6703.0 | 1.0 | 0120O000000LBoCQAW | No data | Nada | 113 | 000 | 2.0 | 0.0 | 2.6 | DON MUY ANTIGUOS | False | Exdonante | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | NaN | 100.0 | Muy bajo | 100.0 | Muy bajo | NaN | 100.0 | 100.0 | 0.0 | 0.0 |
df_contactos.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1803419 entries, 0 to 1803418 Columns: 139 entries, id to msf_commitment__c dtypes: bool(57), float64(36), object(46) memory usage: 1.2+ GB
columnas_contactos = df_contactos.columns.tolist()
columnas_contactos
['id', 'msf_seniority__c', 'npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_entrycampaign__c', 'msf_firstcampaignrecurringdonorchannel__c', 'leadsource', 'msf_firstcampaigncolaborationchannel__c', 'msf_returnedmail__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'npsp__deceased__c', 'msf_begindatemsf__c', 'msf_fechacambiolevelrelacion__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'msf_lastdonationunique__c', 'gender__c', 'msf_crmexternalid__c', 'msf_languagepreferer__c', 'npo02__largestamount__c', 'npo02__smallestamount__c', 'npsp__first_soft_credit_amount__c', 'npo02__lastoppamount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_relationshiplevel__c', 'msf_ltvcont__c', 'msf_ltvdesc__c', 'msf_ltvscore__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'msf_contactdeletereason__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_noagradecimientosmi__c', 'msf_noagradecimientoscp__c', 'msf_noagradecimientosemail__c', 'msf_noagradecimientossms__c', 'msf_noagradecimientostelefono__c', 'msf_nocaptacionfondoscp__c', 'msf_nocaptacionfondosemail__c', 'msf_nocaptacionfondosmi__c', 'msf_nocaptacionfondossms__c', 'msf_nocartasplanrelacioncp__c', 'msf_nocertificadofiscalcp__c', 'msf_nocertificadofiscalemail__c', 'msf_nocertificadofiscalmi__c', 'msf_nocertificadofiscalsms__c', 'msf_nocesionimagenpromocion__c', 'msf_nocomunicacionesonetooneemail__c', 'msf_nocomunicaconesonetoonemi__c', 'msf_nocomunicacionesonetoonecp__c', 'msf_nocomunicaconesonetoonesms__c', 'msf_nocomunicacionesonetoonetelefono__c', 'msf_noemailingstematicosemail__c', 'msf_noencuestaestudioconcursoemail__c', 'msf_noencuestaestudioconcursomi__c', 'msf_nollamadasbienvenidasencuestasotras__c', 'msf_noencuestaestudioconcursosms__c', 'msf_noencuestaestudioconcursotelefono__c', 'msf_noinformaciontestamentaria__c', 'msf_noinvitacioneseventosmi__c', 'msf_noinvitacioneseventoscp__c', 'msf_noinvitacioneseventosemail__c', 'msf_noinvitacioneseventossms__c', 'msf_noinvitacioneseventostelefono__c', 'msf_nomailingstematicoscp__c', 'msf_nomemoriacp__c', 'msf_nomemoriaemail__c', 'msf_nomemoriami__c', 'msf_nomemoriasms__c', 'msf_nomensajesplanrelacionsms__c', 'msf_nomensajestematicosmi__c', 'msf_nomensajestematicossms__c', 'msf_nonewsletteremail__c', 'msf_noplanrelacionemail__c', 'msf_nomensajesplanrelacionmi__c', 'msf_noplanrelaciontelefono__c', 'msf_norevistacp__c', 'msf_norevistaemail__c', 'msf_norevistami__c', 'msf_norevistasms__c', 'msf_notelemarketingcaptacionfondos__c', 'msf_hasfirstdonation__c', 'msf_hasfirstnewrecurringdonation__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_program__c', 'msf_programaherencias__c', 'msf_programais__c', 'msf_pressurecomplaint__c', 'msf_recencydonorcont__c', 'msf_recencydonordesc__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencyrecurringdonordesc__c', 'msf_recencytotalcont__c', 'msf_recencytotalscore__c', 'recordtypeid', 'msf_contactinformationsummary__c', 'msf_percomssummary__c', 'msf_rfvdonor__c', 'msf_rfvrecurringdonor__c', 'title', 'msf_scoringrfvdonor__c', 'msf_scoringrfvrecurringdonor__c', 'msf_scoringrvtotal__c', 'msf_mailingsegment__c', 'msf_legacyconfidentiality__c', 'msf_membertype__c', 'npo02__totaloppamount__c', 'npo02__oppamountthisyear__c', 'npo02__oppamount2yearsago__c', 'npo02__oppamountlastyear__c', 'npo02__best_gift_year_total__c', 'msf_totalfiscaloppamount__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuetotaldesc__c', 'msf_valuedonorcont__c', 'msf_valuedonordesc__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c', 'msf_lifetime__c', 'msf_commitment__c']
# Se revisa el nº total de registros y columnas de la tabla "Contactos"
df_contactos.shape
(1803419, 139)
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_contactos.isnull().sum()
nulos
id 0 msf_seniority__c 0 npo02__best_gift_year__c 0 msf_birthyear__c 0 msf_entrycampaign__c 0 msf_firstcampaignrecurringdonorchannel__c 0 leadsource 0 msf_firstcampaigncolaborationchannel__c 0 msf_returnedmail__c 0 npo02__averageamount__c 0 msf_isactivedonor__c 0 msf_isactiverecurringdonor__c 0 npsp__deceased__c 0 msf_begindatemsf__c 1 msf_fechacambiolevelrelacion__c 2204 msf_datefirstdonation__c 1195087 msf_datefirstrecurringdonorquota__c 858069 msf_datelastrecurringdonorquota__c 858069 msf_datelastdonation__c 1175962 npsp__largest_soft_credit_date__c 1803419 npsp__first_soft_credit_date__c 1803419 msf_entrydatecurrentrecurringdonor__c 809767 npsp__last_soft_credit_date__c 1803419 msf_firstentrydaterecurringdonor__c 809975 npo02__firstclosedate__c 506557 msf_lastrecurringdonationdate__c 1231036 npo02__lastclosedate__c 1803419 msf_lastdonationunique__c 1803419 gender__c 0 msf_crmexternalid__c 0 msf_languagepreferer__c 0 npo02__largestamount__c 0 npo02__smallestamount__c 0 npsp__first_soft_credit_amount__c 1803419 npo02__lastoppamount__c 3626 npsp__last_soft_credit_amount__c 1803419 msf_annualizedquotachange__c 1145924 msf_relationshiplevel__c 0 msf_ltvcont__c 507098 msf_ltvdesc__c 0 msf_ltvscore__c 0 mailingstate 0 npsp__largest_soft_credit_amount__c 1803419 msf_contactdeletereason__c 0 npo02__soft_credit_last_year__c 1803419 npo02__soft_credit_this_year__c 1803419 npo02__soft_credit_two_years_ago__c 1803419 msf_noagradecimientosmi__c 0 msf_noagradecimientoscp__c 0 msf_noagradecimientosemail__c 0 msf_noagradecimientossms__c 0 msf_noagradecimientostelefono__c 0 msf_nocaptacionfondoscp__c 0 msf_nocaptacionfondosemail__c 0 msf_nocaptacionfondosmi__c 0 msf_nocaptacionfondossms__c 0 msf_nocartasplanrelacioncp__c 0 msf_nocertificadofiscalcp__c 0 msf_nocertificadofiscalemail__c 0 msf_nocertificadofiscalmi__c 0 msf_nocertificadofiscalsms__c 0 msf_nocesionimagenpromocion__c 0 msf_nocomunicacionesonetooneemail__c 0 msf_nocomunicaconesonetoonemi__c 0 msf_nocomunicacionesonetoonecp__c 0 msf_nocomunicaconesonetoonesms__c 0 msf_nocomunicacionesonetoonetelefono__c 0 msf_noemailingstematicosemail__c 0 msf_noencuestaestudioconcursoemail__c 0 msf_noencuestaestudioconcursomi__c 0 msf_nollamadasbienvenidasencuestasotras__c 0 msf_noencuestaestudioconcursosms__c 0 msf_noencuestaestudioconcursotelefono__c 0 msf_noinformaciontestamentaria__c 0 msf_noinvitacioneseventosmi__c 0 msf_noinvitacioneseventoscp__c 0 msf_noinvitacioneseventosemail__c 0 msf_noinvitacioneseventossms__c 0 msf_noinvitacioneseventostelefono__c 0 msf_nomailingstematicoscp__c 0 msf_nomemoriacp__c 0 msf_nomemoriaemail__c 0 msf_nomemoriami__c 0 msf_nomemoriasms__c 0 msf_nomensajesplanrelacionsms__c 0 msf_nomensajestematicosmi__c 0 msf_nomensajestematicossms__c 0 msf_nonewsletteremail__c 0 msf_noplanrelacionemail__c 0 msf_nomensajesplanrelacionmi__c 0 msf_noplanrelaciontelefono__c 0 msf_norevistacp__c 0 msf_norevistaemail__c 0 msf_norevistami__c 0 msf_norevistasms__c 0 msf_notelemarketingcaptacionfondos__c 0 msf_hasfirstdonation__c 0 msf_hasfirstnewrecurringdonation__c 0 msf_firstcampaignentryrecurringdonor__c 0 msf_firstcampaingcolaboration__c 0 msf_firstannualizedquota__c 841860 msf_program__c 0 msf_programaherencias__c 0 msf_programais__c 0 msf_pressurecomplaint__c 0 msf_recencydonorcont__c 1177448 msf_recencydonordesc__c 0 msf_recencyrecurringdonorcont__c 868629 msf_recencyrecurringdonordesc__c 0 msf_recencytotalcont__c 507444 msf_recencytotalscore__c 0 recordtypeid 0 msf_contactinformationsummary__c 0 msf_percomssummary__c 0 msf_rfvdonor__c 0 msf_rfvrecurringdonor__c 0 title 0 msf_scoringrfvdonor__c 0 msf_scoringrfvrecurringdonor__c 0 msf_scoringrvtotal__c 0 msf_mailingsegment__c 0 msf_legacyconfidentiality__c 0 msf_membertype__c 0 npo02__totaloppamount__c 1 npo02__oppamountthisyear__c 0 npo02__oppamount2yearsago__c 0 npo02__oppamountlastyear__c 0 npo02__best_gift_year_total__c 507444 msf_totalfiscaloppamount__c 3 msf_lastannualizedquota__c 850655 msf_valuetotalcont__c 450726 msf_valuetotaldesc__c 0 msf_valuedonorcont__c 1178350 msf_valuedonordesc__c 0 msf_lastyeardonorvalue__c 1690288 msf_maximumdonorvalue__c 1177577 msf_averagedonorvalue__c 1177577 msf_lifetime__c 507098 msf_commitment__c 223407 dtype: int64
# Comprobamos si existen duplicados
print(len(df_contactos))
print(len(df_contactos.drop_duplicates()))
1803419 1803419
# Importamos dataset de RECURRING DONATION
df_re_donation = pd.read_parquet("MSF_RecurringDonation.parquet")
df_re_donation.head()
| id | isdeleted | msf_annualizedquota__c | msf_cancelationdate__c | msf_cancelationreason__c | msf_currentcampaign__c | msf_currentleadsource1__c | msf_currentquotamodification__c | msf_leadsource1__c | msf_memberid__c | npe03__amount__c | npe03__contact__c | npe03__date_established__c | npe03__installment_period__c | npe03__last_payment_date__c | npe03__next_payment_date__c | npe03__open_ended_status__c | npe03__paid_amount__c | npe03__recurring_donation_campaign__c | npe03__total_paid_installments__c | npsp4hub__payment_method__c | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | a093Y00001RhiLNQAZ | False | 72.00 | 2011-02-04 | Impago aportaciones | 7013Y000001mqt9QAA | Telemarketing | a1y3Y000004sW6pQAE | Cupón | 9969053 | 6.00 | 0033Y00002uppXLQAY | 2011-01-07 | Monthly | 2011-03-01 | None | Closed | 0.0 | 7013Y000001mqt9QAA | 0.0 | Direct Debit |
| 1 | a093Y00001RhiLVQAZ | False | 72.12 | 2010-11-09 | 3 Obs/Tcs Devueltas | 7013Y000001mqy2QAA | Cupón | a1y3Y000004uVpAQAU | Cupón | 9969066 | 6.01 | 0033Y00002uppXXQAY | 2000-02-01 | Monthly | 2010-12-01 | None | Closed | 0.0 | 7013Y000001mrOjQAI | 0.0 | Direct Debit |
| 2 | a093Y00001RhiLdQAJ | False | 144.24 | 2005-05-09 | Impago aportaciones | 7013Y000001mrOjQAI | Cupón | a1y3Y000004tQbhQAE | Cupón | 9969086 | 12.02 | 0033Y00002uppXqQAI | 2000-02-01 | Monthly | 2005-05-04 | None | Closed | 0.0 | 7013Y000001mrOjQAI | 0.0 | Direct Debit |
| 3 | a093Y00001RhiLoQAJ | False | 72.12 | 2001-05-29 | 3 Obs/Tcs Devueltas | 7013Y000001mrOjQAI | Cupón | a1y3Y000004sun6QAA | Cupón | 9969107 | 6.01 | 0033Y00002uppYCQAY | 2000-02-01 | Monthly | 2001-05-01 | None | Closed | 0.0 | 7013Y000001mrOjQAI | 0.0 | Direct Debit |
| 4 | a093Y00001RhiLtQAJ | False | 72.12 | 2010-05-03 | Incidencias Web | 7013Y000001mrOjQAI | Cupón | a1y3Y000004sD40QAE | Cupón | 9969113 | 6.01 | 0033Y00002uppYIQAY | 2000-02-01 | Monthly | 2010-04-01 | None | Closed | 0.0 | 7013Y000001mrOjQAI | 0.0 | Direct Debit |
df_re_donation.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1198207 entries, 0 to 1198206 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 1198207 non-null object 1 isdeleted 1198207 non-null bool 2 msf_annualizedquota__c 1198207 non-null float64 3 msf_cancelationdate__c 715201 non-null object 4 msf_cancelationreason__c 1198207 non-null object 5 msf_currentcampaign__c 1198207 non-null object 6 msf_currentleadsource1__c 1198207 non-null object 7 msf_currentquotamodification__c 1198207 non-null object 8 msf_leadsource1__c 1198207 non-null object 9 msf_memberid__c 1198207 non-null object 10 npe03__amount__c 1198207 non-null float64 11 npe03__contact__c 1198207 non-null object 12 npe03__date_established__c 1198207 non-null object 13 npe03__installment_period__c 1198207 non-null object 14 npe03__last_payment_date__c 1122597 non-null object 15 npe03__next_payment_date__c 483302 non-null object 16 npe03__open_ended_status__c 1198207 non-null object 17 npe03__paid_amount__c 1196308 non-null float64 18 npe03__recurring_donation_campaign__c 1198207 non-null object 19 npe03__total_paid_installments__c 1196308 non-null float64 20 npsp4hub__payment_method__c 1198207 non-null object dtypes: bool(1), float64(4), object(16) memory usage: 184.0+ MB
columnas_re_donation = df_re_donation.columns.tolist()
columnas_re_donation
['id', 'isdeleted', 'msf_annualizedquota__c', 'msf_cancelationdate__c', 'msf_cancelationreason__c', 'msf_currentcampaign__c', 'msf_currentleadsource1__c', 'msf_currentquotamodification__c', 'msf_leadsource1__c', 'msf_memberid__c', 'npe03__amount__c', 'npe03__contact__c', 'npe03__date_established__c', 'npe03__installment_period__c', 'npe03__last_payment_date__c', 'npe03__next_payment_date__c', 'npe03__open_ended_status__c', 'npe03__paid_amount__c', 'npe03__recurring_donation_campaign__c', 'npe03__total_paid_installments__c', 'npsp4hub__payment_method__c']
# Se revisa el nº total de registros y columnas de la tabla "donaciones recurrentes"
df_re_donation.shape
(1198207, 21)
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_re_donation.isnull().sum()
nulos
id 0 isdeleted 0 msf_annualizedquota__c 0 msf_cancelationdate__c 483006 msf_cancelationreason__c 0 msf_currentcampaign__c 0 msf_currentleadsource1__c 0 msf_currentquotamodification__c 0 msf_leadsource1__c 0 msf_memberid__c 0 npe03__amount__c 0 npe03__contact__c 0 npe03__date_established__c 0 npe03__installment_period__c 0 npe03__last_payment_date__c 75610 npe03__next_payment_date__c 714905 npe03__open_ended_status__c 0 npe03__paid_amount__c 1899 npe03__recurring_donation_campaign__c 0 npe03__total_paid_installments__c 1899 npsp4hub__payment_method__c 0 dtype: int64
# Comprobamos si existen duplicados
print(len(df_re_donation))
print(len(df_re_donation.drop_duplicates()))
1198207 1198207
# Importamos dataset de MODIFICACION DE CUOTA
df_mod_cuota = pd.read_parquet("MSF_QuotaModification.parquet")
df_mod_cuota.head()
| id | isdeleted | name | msf_recurringdonation__c | msf_campaigninfluence__c | msf_changeamount__c | msf_changeannualizedquota__c | msf_changetype__c | msf_leadsource1__c | msf_leadsource2__c | msf_leadsource3__c | msf_newamount__c | msf_newannualizedquota__c | msf_newrecurringperiod__c | msf_contactid__c | msf_changedate__c | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | a1y3Y000001sAtxQAE | False | A - 15948014621775 | a093Y00001RZ7cgQAD | 7013Y000001mquNQAQ | 5.0 | 60.0 | Increase | Teléfono web | Teléfono web | Teléfono | 20.0 | 240.0 | Monthly | 0033Y00002uNQ6CQAW | 2020-04-02 |
| 1 | a1y3Y000001sAu5QAE | False | A - 159480146217713 | a093Y00001RZ7eLQAT | 7013Y000001mrgcQAA | 20.0 | 240.0 | Increase | Telemarketing | Telemarketing | Teléfono | 40.0 | 480.0 | Monthly | 0033Y00002uNQJ6QAO | 2020-05-03 |
| 2 | a1y3Y000001sAuDQAU | False | D - 159480146217721 | a093Y00001RZ7kmQAD | 7013Y000001mrgcQAA | 8.0 | 104.0 | Decrease | Telemarketing | Telemarketing | Teléfono | 68.0 | 136.0 | Semestral | 0033Y00002uNREvQAO | 2020-04-02 |
| 3 | a1y3Y000001sAuLQAU | False | A - 159480146217729 | a093Y00001RZ8NEQA1 | 7013Y000001mqtMQAQ | 35.0 | 35.0 | Increase | Telemarketing | Telemarketing | Teléfono | 110.0 | 110.0 | Yearly | 0033Y00002uNSmdQAG | 2020-03-02 |
| 4 | a1y3Y000001sAuTQAU | False | A - 159480146217737 | a093Y00001RZ9UeQAL | 7013Y000001mrgcQAA | 2.0 | 24.0 | Increase | Telemarketing | Telemarketing | Teléfono | 19.0 | 228.0 | Monthly | 0033Y00002uNV7OQAW | 2020-07-01 |
df_mod_cuota.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2003019 entries, 0 to 2003018 Data columns (total 16 columns): # Column Dtype --- ------ ----- 0 id object 1 isdeleted bool 2 name object 3 msf_recurringdonation__c object 4 msf_campaigninfluence__c object 5 msf_changeamount__c float64 6 msf_changeannualizedquota__c float64 7 msf_changetype__c object 8 msf_leadsource1__c object 9 msf_leadsource2__c object 10 msf_leadsource3__c object 11 msf_newamount__c float64 12 msf_newannualizedquota__c float64 13 msf_newrecurringperiod__c object 14 msf_contactid__c object 15 msf_changedate__c object dtypes: bool(1), float64(4), object(11) memory usage: 231.1+ MB
columnas_mod_cuota = df_mod_cuota.columns.tolist()
columnas_mod_cuota
['id', 'isdeleted', 'name', 'msf_recurringdonation__c', 'msf_campaigninfluence__c', 'msf_changeamount__c', 'msf_changeannualizedquota__c', 'msf_changetype__c', 'msf_leadsource1__c', 'msf_leadsource2__c', 'msf_leadsource3__c', 'msf_newamount__c', 'msf_newannualizedquota__c', 'msf_newrecurringperiod__c', 'msf_contactid__c', 'msf_changedate__c']
# Se revisa el nº total de registros y columnas de la tabla "donaciones recurrentes"
df_mod_cuota.shape
(2003019, 16)
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_mod_cuota.isnull().sum()
nulos
id 0 isdeleted 0 name 0 msf_recurringdonation__c 0 msf_campaigninfluence__c 0 msf_changeamount__c 0 msf_changeannualizedquota__c 0 msf_changetype__c 0 msf_leadsource1__c 0 msf_leadsource2__c 0 msf_leadsource3__c 0 msf_newamount__c 0 msf_newannualizedquota__c 0 msf_newrecurringperiod__c 0 msf_contactid__c 0 msf_changedate__c 186 dtype: int64
# Comprobamos si existen duplicados
print(len(df_mod_cuota))
print(len(df_mod_cuota.drop_duplicates()))
2003019 2003019
# Importamos dataset de CAMPAÑAS
df_Campaign = pd.read_parquet("MSF_Campaign.parquet")
df_Campaign.head()
| id | msf_attribute_1__c | msf_attribute_2__c | msf_attribute_3__c | msf_attribute_4__c | msf_attribute_5__c | msf_campaigndonationreporting__c | msf_campaignentryreporting__c | msf_canalsalidaconcatenado__c | msf_isemergency__c | msf_isonline__c | msf_objective__c | msf_objectivepublic__c | msf_outboundchannel1__c | msf_outboundchannel2__c | msf_ownby__c | msf_previousstepchannel__c | msf_promoterindividual__c | msf_provider__c | msf_segment__c | msf_thematic__c | ownerid | recordtypeid | status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7013Y000001mrHWQAY | Afiliación Leads | 23-Digital Orgánico | 23-Digital Orgánico | Afiliación - | False | Si | Captación de socios o donantes | Afiliación | Digital | Frio individuos | 90 | 0050O000009jTv8QAE | 0120O000000kNMGQA2 | Completed | |||||||||
| 1 | 7013Y000001mrEdQAI | 16-Captación off resto | 18-Captación off resto | Prensa o cupón - | False | No | Captación de socios o donantes | Prensa o cupón | Captación | Frío individuos | 90 | 0050O000009jTv8QAE | 0120O000000kNMGQA2 | Completed | ||||||||||
| 2 | 7013Y000001mrErQAI | 16-Captación off resto | 18-Captación off resto | Prensa o cupón - | False | No | Captación de socios o donantes | Prensa o cupón | Captación | Frío individuos | 05 | 0050O000009jTv8QAE | 0120O000000kNMGQA2 | Completed | ||||||||||
| 3 | 7013Y000001mrLqQAI | Diario de ibiza | 16-Captación off resto | 18-Captación off resto | Encarte - | False | No | Captación de socios o donantes | Encarte | Captación | Frío individuos | 90 | 0050O000009jTv8QAE | 0120O000000kNMGQA2 | Completed | |||||||||
| 4 | 7013Y000001mrFYQAY | 16-Captación off resto | 18-Captación off resto | Mailing - | False | No | Captación de socios o donantes | Mailing | Captación | Frío individuos | 90 | 0050O000009jTv8QAE | 0120O000000kNMGQA2 | Completed |
df_Campaign.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 11501 entries, 0 to 11500 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 11501 non-null object 1 msf_attribute_1__c 11501 non-null object 2 msf_attribute_2__c 11501 non-null object 3 msf_attribute_3__c 11501 non-null object 4 msf_attribute_4__c 11501 non-null object 5 msf_attribute_5__c 11501 non-null object 6 msf_campaigndonationreporting__c 11501 non-null object 7 msf_campaignentryreporting__c 11501 non-null object 8 msf_canalsalidaconcatenado__c 11501 non-null object 9 msf_isemergency__c 11501 non-null bool 10 msf_isonline__c 11501 non-null object 11 msf_objective__c 11501 non-null object 12 msf_objectivepublic__c 11501 non-null object 13 msf_outboundchannel1__c 11501 non-null object 14 msf_outboundchannel2__c 11501 non-null object 15 msf_ownby__c 11501 non-null object 16 msf_previousstepchannel__c 11501 non-null object 17 msf_promoterindividual__c 11501 non-null object 18 msf_provider__c 11501 non-null object 19 msf_segment__c 11501 non-null object 20 msf_thematic__c 11501 non-null object 21 ownerid 11501 non-null object 22 recordtypeid 11501 non-null object 23 status 11501 non-null object dtypes: bool(1), object(23) memory usage: 2.0+ MB
columnas_Campaign = df_Campaign.columns.tolist()
columnas_Campaign
['id', 'msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_campaigndonationreporting__c', 'msf_campaignentryreporting__c', 'msf_canalsalidaconcatenado__c', 'msf_isemergency__c', 'msf_isonline__c', 'msf_objective__c', 'msf_objectivepublic__c', 'msf_outboundchannel1__c', 'msf_outboundchannel2__c', 'msf_ownby__c', 'msf_previousstepchannel__c', 'msf_promoterindividual__c', 'msf_provider__c', 'msf_segment__c', 'msf_thematic__c', 'ownerid', 'recordtypeid', 'status']
# Se revisa el nº total de registros y columnas de la tabla "donaciones recurrentes"
df_Campaign.shape
(11501, 24)
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_Campaign.isnull().sum()
nulos
id 0 msf_attribute_1__c 0 msf_attribute_2__c 0 msf_attribute_3__c 0 msf_attribute_4__c 0 msf_attribute_5__c 0 msf_campaigndonationreporting__c 0 msf_campaignentryreporting__c 0 msf_canalsalidaconcatenado__c 0 msf_isemergency__c 0 msf_isonline__c 0 msf_objective__c 0 msf_objectivepublic__c 0 msf_outboundchannel1__c 0 msf_outboundchannel2__c 0 msf_ownby__c 0 msf_previousstepchannel__c 0 msf_promoterindividual__c 0 msf_provider__c 0 msf_segment__c 0 msf_thematic__c 0 ownerid 0 recordtypeid 0 status 0 dtype: int64
# Comprobamos si existen duplicados
print(len(df_Campaign))
print(len(df_Campaign.drop_duplicates()))
11501 11501
# Importamos dataset de TAREAS
df_tareas2 = pd.read_csv("tarea_aumento_2.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas3 = pd.read_csv("tarea_aumento_3.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas4 = pd.read_csv("tarea_aumento_4.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas5 = pd.read_csv("tarea_aumento_5.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
df_tareas6 = pd.read_csv("tarea_aumento_6.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
C:\Users\marta\AppData\Local\Temp\ipykernel_2548\2290543.py:3: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.
df_tareas3 = pd.read_csv("tarea_aumento_3.csv", names=['msf_Objective__c','msf_CloseType__c','id','ActivityDate','msf_Channel__c','msf_Campaign__c','msf_StartDate__c','Status','WhoId'])
# Unificamos las particiones en una unica tabla TAREA
df_tareas = pd.concat([df_tareas2,df_tareas3,df_tareas4,df_tareas5,df_tareas6])
df_tareas.head()
| msf_Objective__c | msf_CloseType__c | id | ActivityDate | msf_Channel__c | msf_Campaign__c | msf_StartDate__c | Status | WhoId | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Petición económica-Upgrade Socio | Positivo | 00T3Y00005x0TjkUAE | 2021-03-26 | Llamada | 7013Y000001n865QAA | 2021-03-01 | Realizada | 0033Y00002unXvsQAE |
| 1 | Petición económica-Upgrade Socio | Negativo | 00T3Y00005x0UAkUAM | 2021-03-31 | Llamada | 7013Y000001n860QAA | 2021-03-01 | Realizada | 0033Y00002unKClQAM |
| 2 | Petición económica-Upgrade Socio | Positivo | 00T3Y00005x0UIeUAM | 2021-03-23 | Llamada | 7013Y000001n865QAA | 2021-03-01 | Realizada | 0033Y00002upuiLQAQ |
| 3 | Petición económica-Upgrade Socio | Negativo | 00T3Y00005x0UIfUAM | 2021-03-29 | Llamada | 7013Y000001n865QAA | 2021-03-01 | Realizada | 0033Y00002upuinQAA |
| 4 | Petición económica-Upgrade Socio | Negativo | 00T3Y00005x0UJuUAM | 2021-03-04 | Llamada | 7013Y000001n865QAA | 2021-03-01 | Realizada | 0033Y00002v6a4vQAA |
columnas_tareas = df_tareas.columns.tolist()
columnas_tareas
['msf_Objective__c', 'msf_CloseType__c', 'id', 'ActivityDate', 'msf_Channel__c', 'msf_Campaign__c', 'msf_StartDate__c', 'Status', 'WhoId']
# Se revisa el nº total de registros y columnas de la tabla "tareas"
df_tareas.shape
(2612004, 9)
# Se analizan la cantidad de nulos en cada variable del dataset
nulos = df_tareas.isnull().sum()
nulos
msf_Objective__c 0 msf_CloseType__c 62202 id 0 ActivityDate 0 msf_Channel__c 1 msf_Campaign__c 210116 msf_StartDate__c 1526897 Status 0 WhoId 0 dtype: int64
# Comprobamos si existen duplicados
print(len(df_tareas))
print(len(df_tareas.drop_duplicates()))
2612004 2612004
# Contabilizacion de los nulos valores de la variable
def count_nulos(df,variable,list_delete):
'''
Función count_nulos
Uso:
Sirva para realizar un conteo de los registros nulos y vacios de una variable seleccionada del dataframe de entrada.
Parametros entrada:
- df : dataframe de entrada.
- variable: nombre de la variable sobre la que se quiere graficar. Variable tipo categorica o numerica.
Salida:
Frase en la que se detectará el nº de nulos y vacios, asi como su porcentaje del total de la tabla.
'''
nulos = df[[variable]].isnull().sum()[0]
vacios = (df[[var]] == '').sum(axis=0)[0]
print(f"El nº de nulos para la variable {variable} es {nulos}. Lo que supone un {(nulos/df.shape[0])*100}%")
print(f"El nº de vacios para la variable {var} es {vacios}. Lo que supone un {(vacios/df.shape[0])*100}%")
if ((nulos + vacios) / df.shape[0]) *100 > 20:
list_delete.append(var)
return list_delete
# Contabilizacion de los posibles valores de la variable
def freq_variables(df,variable):
'''
Función freq_variables
Uso:
Sirva para realizar un conteo de los posibles valores de una variable seleccionada del dataframe de entrada.
Parametros entrada:
- df : dataframe de entrada.
- variable: nombre de la variable sobre la que se quiere graficar. Variable tipo categorica o numerica.
Salida:
La salida es una tabla que contiene los posibles valores de la variable de entrada y el nº de registros de cada valor en el dataframe.
'''
afreq = df[variable].value_counts()
pfreq = df[variable].value_counts(normalize = True)*100
freq_report = pd.DataFrame({'# Tot':afreq, '% Tot':pfreq})
return freq_report
# Vamos a analizar la tabla recurring donation
df = df_re_donation
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_re_donation=list()
# Vamos a realizar analisis por cada variable
var = "isdeleted"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable isdeleted es 0. Lo que supone un 0.0% El nº de vacios para la variable isdeleted es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1198207 | 100.0 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_re_donation.append(var)
col_to_delete_re_donation
['isdeleted']
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_annualizedquota__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_annualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.00 | 285404 | 23.819257 |
| 60.00 | 129853 | 10.837276 |
| 180.00 | 122956 | 10.261666 |
| 240.00 | 76435 | 6.379115 |
| 144.00 | 57831 | 4.826462 |
| 72.00 | 48674 | 4.062236 |
| 36.00 | 30804 | 2.570841 |
| 360.00 | 28630 | 2.389404 |
| 300.00 | 26549 | 2.215727 |
| 96.00 | 21951 | 1.831987 |
| 72.12 | 21267 | 1.774902 |
| 0.00 | 19245 | 1.606150 |
| 84.00 | 16695 | 1.393332 |
| 100.00 | 15786 | 1.317469 |
| 168.00 | 13694 | 1.142874 |
| 40.00 | 12404 | 1.035213 |
| 20.00 | 11682 | 0.974957 |
| 50.00 | 11473 | 0.957514 |
| 80.00 | 9212 | 0.768815 |
| 600.00 | 8751 | 0.730341 |
| 30.00 | 8515 | 0.710645 |
| 200.00 | 8126 | 0.678180 |
| 10.00 | 8036 | 0.670669 |
| 48.00 | 7544 | 0.629607 |
| 204.00 | 7497 | 0.625685 |
| 51.96 | 6882 | 0.574358 |
| 480.00 | 6742 | 0.562674 |
| 216.00 | 6595 | 0.550406 |
| 12.00 | 6240 | 0.520778 |
| 150.00 | 6048 | 0.504754 |
| 132.00 | 5844 | 0.487729 |
| 60.10 | 5371 | 0.448253 |
| 108.00 | 5310 | 0.443162 |
| 192.00 | 5175 | 0.431895 |
| 156.00 | 5067 | 0.422882 |
| 420.00 | 4893 | 0.408360 |
| 30.05 | 4827 | 0.402852 |
| 120.20 | 4787 | 0.399514 |
| 15.00 | 4202 | 0.350691 |
| 312.00 | 4182 | 0.349021 |
| 216.36 | 4000 | 0.333832 |
| 144.24 | 3959 | 0.330410 |
| 264.00 | 3940 | 0.328825 |
| 720.00 | 3649 | 0.304538 |
| 360.60 | 3442 | 0.287263 |
| 228.00 | 3352 | 0.279751 |
| 160.00 | 3125 | 0.260806 |
| 90.00 | 2964 | 0.247370 |
| 5.00 | 2670 | 0.222833 |
| 276.00 | 2531 | 0.211232 |
| 24.00 | 2191 | 0.182857 |
| 25.00 | 2143 | 0.178851 |
| 1200.00 | 2127 | 0.177515 |
| 140.00 | 2122 | 0.177098 |
| 70.00 | 2020 | 0.168585 |
| 18.03 | 2010 | 0.167751 |
| 400.00 | 1904 | 0.158904 |
| 3.00 | 1601 | 0.133616 |
| 384.00 | 1577 | 0.131613 |
| 540.00 | 1559 | 0.130111 |
| 240.40 | 1520 | 0.126856 |
| 288.00 | 1444 | 0.120513 |
| 75.00 | 1436 | 0.119846 |
| 250.00 | 1227 | 0.102403 |
| 90.15 | 1164 | 0.097145 |
| 336.00 | 1159 | 0.096728 |
| 252.00 | 1142 | 0.095309 |
| 324.00 | 1127 | 0.094057 |
| 260.00 | 946 | 0.078951 |
| 24.04 | 913 | 0.076197 |
| 48.08 | 883 | 0.073693 |
| 721.20 | 878 | 0.073276 |
| 6.00 | 835 | 0.069687 |
| 396.00 | 768 | 0.064096 |
| 500.00 | 746 | 0.062260 |
| 130.00 | 711 | 0.059339 |
| 110.00 | 678 | 0.056585 |
| 280.00 | 652 | 0.054415 |
| 18.00 | 616 | 0.051410 |
| 125.00 | 610 | 0.050909 |
| 840.00 | 609 | 0.050826 |
| 35.00 | 608 | 0.050742 |
| 220.00 | 598 | 0.049908 |
| 660.00 | 575 | 0.047988 |
| 45.00 | 565 | 0.047154 |
| 320.00 | 559 | 0.046653 |
| 150.25 | 500 | 0.041729 |
| 34.86 | 499 | 0.041646 |
| 36.06 | 493 | 0.041145 |
| 36.12 | 493 | 0.041145 |
| 32.00 | 478 | 0.039893 |
| 432.00 | 450 | 0.037556 |
| 900.00 | 428 | 0.035720 |
| 960.00 | 420 | 0.035052 |
| 108.24 | 415 | 0.034635 |
| 139.44 | 413 | 0.034468 |
| 65.00 | 409 | 0.034134 |
| 88.00 | 404 | 0.033717 |
| 28.00 | 402 | 0.033550 |
| 408.00 | 399 | 0.033300 |
| 1000.00 | 387 | 0.032298 |
| 170.00 | 385 | 0.032131 |
| 52.00 | 373 | 0.031130 |
| 42.00 | 356 | 0.029711 |
| 104.04 | 351 | 0.029294 |
| 210.00 | 333 | 0.027792 |
| 8.00 | 328 | 0.027374 |
| 780.00 | 328 | 0.027374 |
| 350.00 | 326 | 0.027207 |
| 173.04 | 316 | 0.026373 |
| 55.00 | 312 | 0.026039 |
| 180.36 | 309 | 0.025789 |
| 624.00 | 299 | 0.024954 |
| 444.00 | 296 | 0.024704 |
| 1080.00 | 294 | 0.024537 |
| 372.00 | 291 | 0.024286 |
| 175.00 | 287 | 0.023952 |
| 1800.00 | 287 | 0.023952 |
| 504.00 | 287 | 0.023952 |
| 800.00 | 279 | 0.023285 |
| 288.48 | 276 | 0.023034 |
| 12.02 | 275 | 0.022951 |
| 56.00 | 271 | 0.022617 |
| 14.00 | 262 | 0.021866 |
| 6.01 | 255 | 0.021282 |
| 22.00 | 252 | 0.021031 |
| 16.00 | 246 | 0.020531 |
| 85.00 | 243 | 0.020280 |
| 165.00 | 242 | 0.020197 |
| 348.00 | 234 | 0.019529 |
| 230.00 | 224 | 0.018695 |
| 456.00 | 220 | 0.018361 |
| 112.00 | 210 | 0.017526 |
| 2400.00 | 210 | 0.017526 |
| 520.00 | 209 | 0.017443 |
| 57.68 | 202 | 0.016859 |
| 180.30 | 201 | 0.016775 |
| 418.32 | 197 | 0.016441 |
| 72.24 | 185 | 0.015440 |
| 103.92 | 184 | 0.015356 |
| 92.00 | 182 | 0.015189 |
| 96.16 | 179 | 0.014939 |
| 1440.00 | 178 | 0.014856 |
| 9.00 | 177 | 0.014772 |
| 68.00 | 170 | 0.014188 |
| 7.00 | 168 | 0.014021 |
| 54.00 | 162 | 0.013520 |
| 36.08 | 159 | 0.013270 |
| 17.00 | 157 | 0.013103 |
| 152.00 | 157 | 0.013103 |
| 105.00 | 155 | 0.012936 |
| 4.00 | 153 | 0.012769 |
| 93.16 | 151 | 0.012602 |
| 340.00 | 143 | 0.011934 |
| 64.00 | 142 | 0.011851 |
| 135.00 | 141 | 0.011768 |
| 104.00 | 140 | 0.011684 |
| 516.00 | 139 | 0.011601 |
| 528.00 | 137 | 0.011434 |
| 128.00 | 135 | 0.011267 |
| 440.00 | 131 | 0.010933 |
| 225.00 | 127 | 0.010599 |
| 1081.80 | 126 | 0.010516 |
| 864.00 | 123 | 0.010265 |
| 1500.00 | 122 | 0.010182 |
| 552.00 | 122 | 0.010182 |
| 190.00 | 120 | 0.010015 |
| 115.00 | 118 | 0.009848 |
| 300.51 | 115 | 0.009598 |
| 224.00 | 115 | 0.009598 |
| 450.00 | 114 | 0.009514 |
| 62.00 | 113 | 0.009431 |
| 124.00 | 112 | 0.009347 |
| 432.72 | 108 | 0.009013 |
| 44.00 | 105 | 0.008763 |
| 148.00 | 103 | 0.008596 |
| 700.00 | 102 | 0.008513 |
| 270.00 | 101 | 0.008429 |
| 38.00 | 101 | 0.008429 |
| 66.00 | 98 | 0.008179 |
| 346.20 | 97 | 0.008095 |
| 492.00 | 93 | 0.007762 |
| 232.00 | 92 | 0.007678 |
| 21.00 | 92 | 0.007678 |
| 95.00 | 91 | 0.007595 |
| 11.00 | 91 | 0.007595 |
| 115.40 | 90 | 0.007511 |
| 1020.00 | 89 | 0.007428 |
| 468.00 | 88 | 0.007344 |
| 26.00 | 85 | 0.007094 |
| 380.00 | 84 | 0.007010 |
| 3600.00 | 83 | 0.006927 |
| 28.85 | 82 | 0.006844 |
| 108.12 | 80 | 0.006677 |
| 460.00 | 80 | 0.006677 |
| 76.00 | 80 | 0.006677 |
| 60.12 | 79 | 0.006593 |
| 576.00 | 77 | 0.006426 |
| 601.00 | 77 | 0.006426 |
| 34.85 | 77 | 0.006426 |
| 184.00 | 74 | 0.006176 |
| 42.07 | 72 | 0.006009 |
| 310.00 | 71 | 0.005926 |
| 192.32 | 70 | 0.005842 |
| 865.44 | 70 | 0.005842 |
| 155.00 | 69 | 0.005759 |
| 84.14 | 68 | 0.005675 |
| 480.80 | 68 | 0.005675 |
| 300.50 | 68 | 0.005675 |
| 136.00 | 67 | 0.005592 |
| 180.24 | 66 | 0.005508 |
| 330.00 | 66 | 0.005508 |
| 14.42 | 65 | 0.005425 |
| 12000.00 | 65 | 0.005425 |
| 2000.00 | 64 | 0.005341 |
| 164.00 | 63 | 0.005258 |
| 74.00 | 62 | 0.005174 |
| 13.00 | 61 | 0.005091 |
| 275.00 | 61 | 0.005091 |
| 390.00 | 58 | 0.004841 |
| 1320.00 | 58 | 0.004841 |
| 601.01 | 57 | 0.004757 |
| 139.40 | 57 | 0.004757 |
| 34.00 | 57 | 0.004757 |
| 54.09 | 56 | 0.004674 |
| 78.00 | 55 | 0.004590 |
| 27.00 | 53 | 0.004423 |
| 3000.00 | 52 | 0.004340 |
| 33.00 | 51 | 0.004256 |
| 57.70 | 50 | 0.004173 |
| 145.00 | 50 | 0.004173 |
| 576.96 | 50 | 0.004173 |
| 560.00 | 50 | 0.004173 |
| 126.00 | 49 | 0.004089 |
| 1442.40 | 48 | 0.004006 |
| 102.00 | 48 | 0.004006 |
| 640.00 | 47 | 0.003923 |
| 550.00 | 46 | 0.003839 |
| 564.00 | 43 | 0.003589 |
| 372.64 | 43 | 0.003589 |
| 248.00 | 43 | 0.003589 |
| 168.28 | 43 | 0.003589 |
| 212.00 | 42 | 0.003505 |
| 208.00 | 42 | 0.003505 |
| 63.00 | 42 | 0.003505 |
| 116.00 | 42 | 0.003505 |
| 648.00 | 42 | 0.003505 |
| 744.00 | 41 | 0.003422 |
| 12.04 | 40 | 0.003338 |
| 185.00 | 40 | 0.003338 |
| 82.00 | 39 | 0.003255 |
| 1803.00 | 39 | 0.003255 |
| 6000.00 | 38 | 0.003171 |
| 93.15 | 37 | 0.003088 |
| 620.00 | 37 | 0.003088 |
| 172.00 | 37 | 0.003088 |
| 162.00 | 37 | 0.003088 |
| 504.84 | 37 | 0.003088 |
| 361.44 | 36 | 0.003004 |
| 39.00 | 35 | 0.002921 |
| 364.00 | 35 | 0.002921 |
| 17.32 | 35 | 0.002921 |
| 290.00 | 34 | 0.002838 |
| 108.18 | 34 | 0.002838 |
| 84.60 | 32 | 0.002671 |
| 23.00 | 32 | 0.002671 |
| 75.96 | 32 | 0.002671 |
| 37.00 | 32 | 0.002671 |
| 188.00 | 31 | 0.002587 |
| 612.00 | 31 | 0.002587 |
| 1560.00 | 30 | 0.002504 |
| 792.00 | 30 | 0.002504 |
| 365.00 | 30 | 0.002504 |
| 196.00 | 30 | 0.002504 |
| 176.00 | 30 | 0.002504 |
| 636.00 | 29 | 0.002420 |
| 63.96 | 29 | 0.002420 |
| 375.00 | 28 | 0.002337 |
| 98.00 | 27 | 0.002253 |
| 1008.00 | 27 | 0.002253 |
| 325.00 | 27 | 0.002253 |
| 370.00 | 27 | 0.002253 |
| 650.00 | 26 | 0.002170 |
| 236.00 | 26 | 0.002170 |
| 46.00 | 25 | 0.002086 |
| 692.40 | 24 | 0.002003 |
| 240.41 | 24 | 0.002003 |
| 180.32 | 23 | 0.001920 |
| 36.04 | 23 | 0.001920 |
| 78.13 | 22 | 0.001836 |
| 67.00 | 22 | 0.001836 |
| 252.48 | 22 | 0.001836 |
| 392.00 | 21 | 0.001753 |
| 750.00 | 21 | 0.001753 |
| 936.00 | 21 | 0.001753 |
| 94.00 | 21 | 0.001753 |
| 1600.00 | 20 | 0.001669 |
| 272.00 | 20 | 0.001669 |
| 235.00 | 20 | 0.001669 |
| 215.00 | 20 | 0.001669 |
| 120.48 | 20 | 0.001669 |
| 1117.92 | 20 | 0.001669 |
| 174.00 | 19 | 0.001586 |
| 109.44 | 19 | 0.001586 |
| 60.24 | 19 | 0.001586 |
| 1140.00 | 19 | 0.001586 |
| 3606.12 | 19 | 0.001586 |
| 77.00 | 19 | 0.001586 |
| 2160.00 | 18 | 0.001502 |
| 9.02 | 18 | 0.001502 |
| 425.00 | 17 | 0.001419 |
| 108.20 | 17 | 0.001419 |
| 31.00 | 17 | 0.001419 |
| 60.08 | 17 | 0.001419 |
| 86.00 | 17 | 0.001419 |
| 15.03 | 17 | 0.001419 |
| 672.00 | 17 | 0.001419 |
| 3.01 | 17 | 0.001419 |
| 8.66 | 16 | 0.001335 |
| 418.20 | 16 | 0.001335 |
| 21.60 | 16 | 0.001335 |
| 684.00 | 16 | 0.001335 |
| 180.28 | 16 | 0.001335 |
| 760.00 | 15 | 0.001252 |
| 230.80 | 15 | 0.001252 |
| 51.00 | 15 | 0.001252 |
| 28.84 | 15 | 0.001252 |
| 205.00 | 15 | 0.001252 |
| 1680.00 | 14 | 0.001168 |
| 86.52 | 14 | 0.001168 |
| 680.00 | 14 | 0.001168 |
| 649.08 | 14 | 0.001168 |
| 7200.00 | 14 | 0.001168 |
| 4.33 | 14 | 0.001168 |
| 73.00 | 14 | 0.001168 |
| 182.40 | 14 | 0.001168 |
| 244.00 | 14 | 0.001168 |
| 1260.00 | 14 | 0.001168 |
| 1152.00 | 14 | 0.001168 |
| 5196.00 | 13 | 0.001085 |
| 3900.00 | 13 | 0.001085 |
| 87.00 | 13 | 0.001085 |
| 53.00 | 13 | 0.001085 |
| 4800.00 | 13 | 0.001085 |
| 114.00 | 13 | 0.001085 |
| 138.00 | 13 | 0.001085 |
| 69.72 | 13 | 0.001085 |
| 61.00 | 13 | 0.001085 |
| 4000.00 | 13 | 0.001085 |
| 1202.04 | 12 | 0.001001 |
| 732.00 | 12 | 0.001001 |
| 84.16 | 12 | 0.001001 |
| 195.00 | 12 | 0.001001 |
| 268.00 | 12 | 0.001001 |
| 2163.60 | 12 | 0.001001 |
| 58.00 | 12 | 0.001001 |
| 360.61 | 12 | 0.001001 |
| 210.35 | 12 | 0.001001 |
| 120.12 | 12 | 0.001001 |
| 756.00 | 12 | 0.001001 |
| 90.12 | 12 | 0.001001 |
| 7.20 | 12 | 0.001001 |
| 304.00 | 12 | 0.001001 |
| 30.12 | 11 | 0.000918 |
| 696.00 | 11 | 0.000918 |
| 122.00 | 11 | 0.000918 |
| 768.00 | 11 | 0.000918 |
| 292.00 | 10 | 0.000835 |
| 284.00 | 10 | 0.000835 |
| 198.00 | 10 | 0.000835 |
| 106.00 | 10 | 0.000835 |
| 1400.00 | 10 | 0.000835 |
| 1920.00 | 10 | 0.000835 |
| 410.00 | 10 | 0.000835 |
| 57.00 | 10 | 0.000835 |
| 580.00 | 10 | 0.000835 |
| 19.00 | 10 | 0.000835 |
| 344.00 | 10 | 0.000835 |
| 84.12 | 10 | 0.000835 |
| 316.00 | 10 | 0.000835 |
| 134.00 | 10 | 0.000835 |
| 255.00 | 9 | 0.000751 |
| 804.00 | 9 | 0.000751 |
| 81.00 | 9 | 0.000751 |
| 2100.00 | 9 | 0.000751 |
| 2040.00 | 9 | 0.000751 |
| 296.00 | 9 | 0.000751 |
| 66.11 | 9 | 0.000751 |
| 470.00 | 9 | 0.000751 |
| 7.50 | 9 | 0.000751 |
| 101.00 | 9 | 0.000751 |
| 142.00 | 9 | 0.000751 |
| 880.00 | 9 | 0.000751 |
| 1032.00 | 9 | 0.000751 |
| 1100.00 | 8 | 0.000668 |
| 47.00 | 8 | 0.000668 |
| 1380.00 | 8 | 0.000668 |
| 171.96 | 8 | 0.000668 |
| 29.00 | 8 | 0.000668 |
| 83.00 | 8 | 0.000668 |
| 202.00 | 8 | 0.000668 |
| 222.00 | 8 | 0.000668 |
| 115.36 | 8 | 0.000668 |
| 90.36 | 8 | 0.000668 |
| 7.21 | 8 | 0.000668 |
| 41.00 | 8 | 0.000668 |
| 888.00 | 7 | 0.000584 |
| 372.60 | 7 | 0.000584 |
| 336.56 | 7 | 0.000584 |
| 816.00 | 7 | 0.000584 |
| 5000.00 | 7 | 0.000584 |
| 50.40 | 7 | 0.000584 |
| 93.00 | 7 | 0.000584 |
| 123.00 | 7 | 0.000584 |
| 256.00 | 7 | 0.000584 |
| 430.00 | 7 | 0.000584 |
| 43.32 | 6 | 0.000501 |
| 154.00 | 6 | 0.000501 |
| 108.16 | 6 | 0.000501 |
| 28.80 | 6 | 0.000501 |
| 308.00 | 6 | 0.000501 |
| 1212.00 | 6 | 0.000501 |
| 850.00 | 6 | 0.000501 |
| 57.72 | 6 | 0.000501 |
| 820.00 | 6 | 0.000501 |
| 96.12 | 6 | 0.000501 |
| 182.00 | 6 | 0.000501 |
| 14.40 | 6 | 0.000501 |
| 332.00 | 6 | 0.000501 |
| 984.00 | 6 | 0.000501 |
| 50.52 | 6 | 0.000501 |
| 376.00 | 6 | 0.000501 |
| 920.00 | 6 | 0.000501 |
| 285.00 | 6 | 0.000501 |
| 588.00 | 6 | 0.000501 |
| 328.00 | 6 | 0.000501 |
| 43.00 | 6 | 0.000501 |
| 57.69 | 6 | 0.000501 |
| 852.00 | 5 | 0.000417 |
| 7212.12 | 5 | 0.000417 |
| 540.96 | 5 | 0.000417 |
| 305.00 | 5 | 0.000417 |
| 4200.00 | 5 | 0.000417 |
| 91.00 | 5 | 0.000417 |
| 14.44 | 5 | 0.000417 |
| 286.00 | 5 | 0.000417 |
| 1009.68 | 5 | 0.000417 |
| 721.22 | 5 | 0.000417 |
| 79.32 | 5 | 0.000417 |
| 99.96 | 5 | 0.000417 |
| 99.00 | 5 | 0.000417 |
| 1120.00 | 5 | 0.000417 |
| 1040.00 | 5 | 0.000417 |
| 97.00 | 5 | 0.000417 |
| 448.00 | 5 | 0.000417 |
| 368.00 | 5 | 0.000417 |
| 132.20 | 5 | 0.000417 |
| 40.05 | 5 | 0.000417 |
| 722.88 | 5 | 0.000417 |
| 416.00 | 5 | 0.000417 |
| 52.89 | 5 | 0.000417 |
| 151.00 | 5 | 0.000417 |
| 186.00 | 5 | 0.000417 |
| 252.36 | 5 | 0.000417 |
| 1620.00 | 5 | 0.000417 |
| 841.40 | 4 | 0.000334 |
| 18.04 | 4 | 0.000334 |
| 601.02 | 4 | 0.000334 |
| 30.06 | 4 | 0.000334 |
| 828.00 | 4 | 0.000334 |
| 876.00 | 4 | 0.000334 |
| 475.00 | 4 | 0.000334 |
| 510.00 | 4 | 0.000334 |
| 937.56 | 4 | 0.000334 |
| 59.00 | 4 | 0.000334 |
| 924.00 | 4 | 0.000334 |
| 149.00 | 4 | 0.000334 |
| 0.72 | 4 | 0.000334 |
| 300.52 | 4 | 0.000334 |
| 1.00 | 4 | 0.000334 |
| 1202.00 | 4 | 0.000334 |
| 1250.00 | 4 | 0.000334 |
| 450.75 | 4 | 0.000334 |
| 30.04 | 4 | 0.000334 |
| 49.00 | 4 | 0.000334 |
| 388.00 | 4 | 0.000334 |
| 118.00 | 4 | 0.000334 |
| 194.00 | 4 | 0.000334 |
| 740.00 | 4 | 0.000334 |
| 625.00 | 4 | 0.000334 |
| 1128.00 | 4 | 0.000334 |
| 240.36 | 4 | 0.000334 |
| 100.15 | 4 | 0.000334 |
| 346.08 | 4 | 0.000334 |
| 2884.92 | 4 | 0.000334 |
| 352.00 | 4 | 0.000334 |
| 45.07 | 4 | 0.000334 |
| 1464.00 | 4 | 0.000334 |
| 333.00 | 4 | 0.000334 |
| 424.00 | 4 | 0.000334 |
| 912.00 | 4 | 0.000334 |
| 345.00 | 4 | 0.000334 |
| 12.50 | 4 | 0.000334 |
| 245.00 | 4 | 0.000334 |
| 161.00 | 4 | 0.000334 |
| 201.00 | 4 | 0.000334 |
| 1298.16 | 3 | 0.000250 |
| 315.00 | 3 | 0.000250 |
| 60.01 | 3 | 0.000250 |
| 198.33 | 3 | 0.000250 |
| 93.72 | 3 | 0.000250 |
| 265.00 | 3 | 0.000250 |
| 306.00 | 3 | 0.000250 |
| 1300.00 | 3 | 0.000250 |
| 109.00 | 3 | 0.000250 |
| 396.60 | 3 | 0.000250 |
| 1081.84 | 3 | 0.000250 |
| 70.10 | 3 | 0.000250 |
| 35.05 | 3 | 0.000250 |
| 274.00 | 3 | 0.000250 |
| 488.00 | 3 | 0.000250 |
| 1860.00 | 3 | 0.000250 |
| 234.00 | 3 | 0.000250 |
| 1296.00 | 3 | 0.000250 |
| 1284.00 | 3 | 0.000250 |
| 708.00 | 3 | 0.000250 |
| 1355.88 | 3 | 0.000250 |
| 1224.00 | 3 | 0.000250 |
| 1730.88 | 3 | 0.000250 |
| 3606.00 | 3 | 0.000250 |
| 996.00 | 3 | 0.000250 |
| 262.00 | 3 | 0.000250 |
| 113.00 | 3 | 0.000250 |
| 901.52 | 3 | 0.000250 |
| 1980.00 | 3 | 0.000250 |
| 187.00 | 3 | 0.000250 |
| 4327.32 | 3 | 0.000250 |
| 103.00 | 3 | 0.000250 |
| 725.00 | 3 | 0.000250 |
| 43.20 | 3 | 0.000250 |
| 166.00 | 3 | 0.000250 |
| 71.00 | 3 | 0.000250 |
| 45.08 | 3 | 0.000250 |
| 18000.00 | 3 | 0.000250 |
| 21.64 | 3 | 0.000250 |
| 240.96 | 3 | 0.000250 |
| 2880.00 | 3 | 0.000250 |
| 8000.00 | 3 | 0.000250 |
| 72000.00 | 3 | 0.000250 |
| 436.00 | 3 | 0.000250 |
| 89.00 | 3 | 0.000250 |
| 111.00 | 3 | 0.000250 |
| 961.64 | 3 | 0.000250 |
| 324.60 | 3 | 0.000250 |
| 52.02 | 3 | 0.000250 |
| 54.12 | 3 | 0.000250 |
| 415.00 | 3 | 0.000250 |
| 137.00 | 3 | 0.000250 |
| 9.01 | 3 | 0.000250 |
| 7212.00 | 3 | 0.000250 |
| 87.96 | 3 | 0.000250 |
| 121.00 | 3 | 0.000250 |
| 64.92 | 3 | 0.000250 |
| 24000.00 | 3 | 0.000250 |
| 480.81 | 3 | 0.000250 |
| 2404.04 | 3 | 0.000250 |
| 186.32 | 3 | 0.000250 |
| 159.96 | 3 | 0.000250 |
| 2.00 | 3 | 0.000250 |
| 4320.00 | 2 | 0.000167 |
| 420.60 | 2 | 0.000167 |
| 39.96 | 2 | 0.000167 |
| 793.32 | 2 | 0.000167 |
| 3200.00 | 2 | 0.000167 |
| 294.00 | 2 | 0.000167 |
| 346.00 | 2 | 0.000167 |
| 99.60 | 2 | 0.000167 |
| 25.98 | 2 | 0.000167 |
| 36.66 | 2 | 0.000167 |
| 79.00 | 2 | 0.000167 |
| 446.00 | 2 | 0.000167 |
| 1.20 | 2 | 0.000167 |
| 94.96 | 2 | 0.000167 |
| 93.24 | 2 | 0.000167 |
| 540.60 | 2 | 0.000167 |
| 75.72 | 2 | 0.000167 |
| 2700.00 | 2 | 0.000167 |
| 43.27 | 2 | 0.000167 |
| 335.00 | 2 | 0.000167 |
| 1204.00 | 2 | 0.000167 |
| 8.67 | 2 | 0.000167 |
| 26.44 | 2 | 0.000167 |
| 525.00 | 2 | 0.000167 |
| 173.00 | 2 | 0.000167 |
| 530.00 | 2 | 0.000167 |
| 65.10 | 2 | 0.000167 |
| 122.64 | 2 | 0.000167 |
| 860.00 | 2 | 0.000167 |
| 2500.00 | 2 | 0.000167 |
| 356.00 | 2 | 0.000167 |
| 42.05 | 2 | 0.000167 |
| 59.08 | 2 | 0.000167 |
| 412.00 | 2 | 0.000167 |
| 2280.00 | 2 | 0.000167 |
| 120.01 | 2 | 0.000167 |
| 36000.00 | 2 | 0.000167 |
| 69.00 | 2 | 0.000167 |
| 240.60 | 2 | 0.000167 |
| 79.92 | 2 | 0.000167 |
| 223.92 | 2 | 0.000167 |
| 1104.00 | 2 | 0.000167 |
| 144.48 | 2 | 0.000167 |
| 153.00 | 2 | 0.000167 |
| 177.00 | 2 | 0.000167 |
| 157.00 | 2 | 0.000167 |
| 167.00 | 2 | 0.000167 |
| 189.00 | 2 | 0.000167 |
| 570.00 | 2 | 0.000167 |
| 127.00 | 2 | 0.000167 |
| 198.36 | 2 | 0.000167 |
| 240.12 | 2 | 0.000167 |
| 148.24 | 2 | 0.000167 |
| 5400.00 | 2 | 0.000167 |
| 60.05 | 2 | 0.000167 |
| 146.00 | 2 | 0.000167 |
| 211.56 | 2 | 0.000167 |
| 100.01 | 2 | 0.000167 |
| 123.96 | 2 | 0.000167 |
| 187.20 | 2 | 0.000167 |
| 158.00 | 2 | 0.000167 |
| 223.20 | 2 | 0.000167 |
| 55.92 | 2 | 0.000167 |
| 295.00 | 2 | 0.000167 |
| 458.00 | 2 | 0.000167 |
| 25.20 | 2 | 0.000167 |
| 585.00 | 2 | 0.000167 |
| 366.00 | 2 | 0.000167 |
| 51.60 | 2 | 0.000167 |
| 2520.00 | 2 | 0.000167 |
| 2340.00 | 2 | 0.000167 |
| 139.00 | 2 | 0.000167 |
| 2200.00 | 2 | 0.000167 |
| 1360.00 | 2 | 0.000167 |
| 961.60 | 2 | 0.000167 |
| 138.23 | 2 | 0.000167 |
| 1596.00 | 2 | 0.000167 |
| 1.80 | 2 | 0.000167 |
| 200.04 | 2 | 0.000167 |
| 237.96 | 2 | 0.000167 |
| 27.05 | 2 | 0.000167 |
| 282.00 | 2 | 0.000167 |
| 1202.02 | 2 | 0.000167 |
| 249.96 | 2 | 0.000167 |
| 2640.00 | 2 | 0.000167 |
| 420.71 | 2 | 0.000167 |
| 30.50 | 2 | 0.000167 |
| 90.16 | 2 | 0.000167 |
| 613.08 | 2 | 0.000167 |
| 159.40 | 2 | 0.000167 |
| 144.12 | 2 | 0.000167 |
| 1056.00 | 2 | 0.000167 |
| 126.21 | 2 | 0.000167 |
| 1803.04 | 2 | 0.000167 |
| 92.12 | 2 | 0.000167 |
| 810.00 | 2 | 0.000167 |
| 6.02 | 2 | 0.000167 |
| 302.00 | 2 | 0.000167 |
| 2524.20 | 2 | 0.000167 |
| 74.52 | 2 | 0.000167 |
| 77.10 | 1 | 0.000083 |
| 2760.00 | 1 | 0.000083 |
| 208.08 | 1 | 0.000083 |
| 80.40 | 1 | 0.000083 |
| 6600.00 | 1 | 0.000083 |
| 669.60 | 1 | 0.000083 |
| 40.06 | 1 | 0.000083 |
| 160.08 | 1 | 0.000083 |
| 2328.00 | 1 | 0.000083 |
| 558.00 | 1 | 0.000083 |
| 156.24 | 1 | 0.000083 |
| 216.72 | 1 | 0.000083 |
| 484.00 | 1 | 0.000083 |
| 382.00 | 1 | 0.000083 |
| 374.00 | 1 | 0.000083 |
| 343.92 | 1 | 0.000083 |
| 176.24 | 1 | 0.000083 |
| 139.36 | 1 | 0.000083 |
| 464.00 | 1 | 0.000083 |
| 630.00 | 1 | 0.000083 |
| 32.05 | 1 | 0.000083 |
| 1512.00 | 1 | 0.000083 |
| 115.32 | 1 | 0.000083 |
| 214.00 | 1 | 0.000083 |
| 385.00 | 1 | 0.000083 |
| 152.24 | 1 | 0.000083 |
| 10.50 | 1 | 0.000083 |
| 112.12 | 1 | 0.000083 |
| 289.00 | 1 | 0.000083 |
| 300000.00 | 1 | 0.000083 |
| 164.24 | 1 | 0.000083 |
| 140.20 | 1 | 0.000083 |
| 100.10 | 1 | 0.000083 |
| 264.24 | 1 | 0.000083 |
| 238.80 | 1 | 0.000083 |
| 74.40 | 1 | 0.000083 |
| 14.02 | 1 | 0.000083 |
| 2016.00 | 1 | 0.000083 |
| 79.20 | 1 | 0.000083 |
| 211.52 | 1 | 0.000083 |
| 278.00 | 1 | 0.000083 |
| 252.40 | 1 | 0.000083 |
| 720.80 | 1 | 0.000083 |
| 102.17 | 1 | 0.000083 |
| 128.20 | 1 | 0.000083 |
| 19.50 | 1 | 0.000083 |
| 199.20 | 1 | 0.000083 |
| 842.88 | 1 | 0.000083 |
| 4080.00 | 1 | 0.000083 |
| 224.24 | 1 | 0.000083 |
| 532.00 | 1 | 0.000083 |
| 1752.00 | 1 | 0.000083 |
| 2800.00 | 1 | 0.000083 |
| 245.16 | 1 | 0.000083 |
| 117.00 | 1 | 0.000083 |
| 3020.00 | 1 | 0.000083 |
| 824.56 | 1 | 0.000083 |
| 258.00 | 1 | 0.000083 |
| 168.24 | 1 | 0.000083 |
| 364.80 | 1 | 0.000083 |
| 243.96 | 1 | 0.000083 |
| 402.00 | 1 | 0.000083 |
| 1992.00 | 1 | 0.000083 |
| 90.14 | 1 | 0.000083 |
| 206.00 | 1 | 0.000083 |
| 306.51 | 1 | 0.000083 |
| 59.88 | 1 | 0.000083 |
| 35.88 | 1 | 0.000083 |
| 211.00 | 1 | 0.000083 |
| 253.00 | 1 | 0.000083 |
| 300.48 | 1 | 0.000083 |
| 1280.00 | 1 | 0.000083 |
| 1092.00 | 1 | 0.000083 |
| 181.00 | 1 | 0.000083 |
| 468.12 | 1 | 0.000083 |
| 4500.00 | 1 | 0.000083 |
| 66.10 | 1 | 0.000083 |
| 968.00 | 1 | 0.000083 |
| 973.20 | 1 | 0.000083 |
| 280.40 | 1 | 0.000083 |
| 1442.43 | 1 | 0.000083 |
| 428.00 | 1 | 0.000083 |
| 536.00 | 1 | 0.000083 |
| 318.00 | 1 | 0.000083 |
| 544.00 | 1 | 0.000083 |
| 135.96 | 1 | 0.000083 |
| 126.84 | 1 | 0.000083 |
| 218.00 | 1 | 0.000083 |
| 160.25 | 1 | 0.000083 |
| 338.00 | 1 | 0.000083 |
| 322.00 | 1 | 0.000083 |
| 63.60 | 1 | 0.000083 |
| 1160.00 | 1 | 0.000083 |
| 1116.00 | 1 | 0.000083 |
| 1117.80 | 1 | 0.000083 |
| 505.00 | 1 | 0.000083 |
| 144.20 | 1 | 0.000083 |
| 972.00 | 1 | 0.000083 |
| 1044.00 | 1 | 0.000083 |
| 38.40 | 1 | 0.000083 |
| 812.00 | 1 | 0.000083 |
| 1750.00 | 1 | 0.000083 |
| 212.76 | 1 | 0.000083 |
| 901.20 | 1 | 0.000083 |
| 160.20 | 1 | 0.000083 |
| 210.32 | 1 | 0.000083 |
| 323.00 | 1 | 0.000083 |
| 641.00 | 1 | 0.000083 |
| 1356.00 | 1 | 0.000083 |
| 3800.00 | 1 | 0.000083 |
| 36.05 | 1 | 0.000083 |
| 798.00 | 1 | 0.000083 |
| 302.88 | 1 | 0.000083 |
| 320.50 | 1 | 0.000083 |
| 260.40 | 1 | 0.000083 |
| 728.00 | 1 | 0.000083 |
| 164.40 | 1 | 0.000083 |
| 451.00 | 1 | 0.000083 |
| 273.00 | 1 | 0.000083 |
| 572.00 | 1 | 0.000083 |
| 110.40 | 1 | 0.000083 |
| 126.15 | 1 | 0.000083 |
| 2884.80 | 1 | 0.000083 |
| 692.28 | 1 | 0.000083 |
| 311.00 | 1 | 0.000083 |
| 188.04 | 1 | 0.000083 |
| 281.00 | 1 | 0.000083 |
| 1584.00 | 1 | 0.000083 |
| 100.92 | 1 | 0.000083 |
| 382.08 | 1 | 0.000083 |
| 76.12 | 1 | 0.000083 |
| 60.15 | 1 | 0.000083 |
| 480.24 | 1 | 0.000083 |
| 1502.40 | 1 | 0.000083 |
| 1908.00 | 1 | 0.000083 |
| 480.60 | 1 | 0.000083 |
| 9840.00 | 1 | 0.000083 |
| 3360.00 | 1 | 0.000083 |
| 40.20 | 1 | 0.000083 |
| 133.32 | 1 | 0.000083 |
| 592.00 | 1 | 0.000083 |
| 207.00 | 1 | 0.000083 |
| 741.00 | 1 | 0.000083 |
| 3300.00 | 1 | 0.000083 |
| 70.01 | 1 | 0.000083 |
| 247.68 | 1 | 0.000083 |
| 1644.00 | 1 | 0.000083 |
| 55.56 | 1 | 0.000083 |
| 169.00 | 1 | 0.000083 |
| 120.10 | 1 | 0.000083 |
| 199.00 | 1 | 0.000083 |
| 3180.00 | 1 | 0.000083 |
| 980.00 | 1 | 0.000083 |
| 158.64 | 1 | 0.000083 |
| 1803.03 | 1 | 0.000083 |
| 608.00 | 1 | 0.000083 |
| 340.08 | 1 | 0.000083 |
| 438.00 | 1 | 0.000083 |
| 466.64 | 1 | 0.000083 |
| 901.00 | 1 | 0.000083 |
| 107.00 | 1 | 0.000083 |
| 17.34 | 1 | 0.000083 |
| 565.00 | 1 | 0.000083 |
| 139.92 | 1 | 0.000083 |
| 30.40 | 1 | 0.000083 |
| 147.00 | 1 | 0.000083 |
| 675.00 | 1 | 0.000083 |
| 409.44 | 1 | 0.000083 |
| 238.00 | 1 | 0.000083 |
| 8400.00 | 1 | 0.000083 |
| 90.75 | 1 | 0.000083 |
| 54.08 | 1 | 0.000083 |
| 1226.04 | 1 | 0.000083 |
| 66.66 | 1 | 0.000083 |
| 468.84 | 1 | 0.000083 |
| 51.09 | 1 | 0.000083 |
| 54.93 | 1 | 0.000083 |
| 9.32 | 1 | 0.000083 |
| 245.20 | 1 | 0.000083 |
| 143.04 | 1 | 0.000083 |
| 9.60 | 1 | 0.000083 |
| 54.60 | 1 | 0.000083 |
| 18030.36 | 1 | 0.000083 |
| 70.12 | 1 | 0.000083 |
| 165.84 | 1 | 0.000083 |
| 1153.92 | 1 | 0.000083 |
| 69.96 | 1 | 0.000083 |
| 42.08 | 1 | 0.000083 |
| 3137.28 | 1 | 0.000083 |
| 123.80 | 1 | 0.000083 |
| 219.36 | 1 | 0.000083 |
| 1478.52 | 1 | 0.000083 |
| 75.13 | 1 | 0.000083 |
| 197.52 | 1 | 0.000083 |
| 162.24 | 1 | 0.000083 |
| 99.12 | 1 | 0.000083 |
| 1586.64 | 1 | 0.000083 |
| 512.08 | 1 | 0.000083 |
| 8654.64 | 1 | 0.000083 |
| 6010.12 | 1 | 0.000083 |
| 594.96 | 1 | 0.000083 |
| 175.32 | 1 | 0.000083 |
| 105.24 | 1 | 0.000083 |
| 2352.00 | 1 | 0.000083 |
| 451.96 | 1 | 0.000083 |
| 201.96 | 1 | 0.000083 |
| 450.76 | 1 | 0.000083 |
| 336.36 | 1 | 0.000083 |
| 183.48 | 1 | 0.000083 |
| 129.84 | 1 | 0.000083 |
| 230.76 | 1 | 0.000083 |
| 634.68 | 1 | 0.000083 |
| 46.92 | 1 | 0.000083 |
| 86.40 | 1 | 0.000083 |
| 447.12 | 1 | 0.000083 |
| 1442.44 | 1 | 0.000083 |
| 270.46 | 1 | 0.000083 |
| 31.24 | 1 | 0.000083 |
| 7.22 | 1 | 0.000083 |
| 259.48 | 1 | 0.000083 |
| 80.16 | 1 | 0.000083 |
| 3606.24 | 1 | 0.000083 |
| 29.99 | 1 | 0.000083 |
| 468.72 | 1 | 0.000083 |
| 156.28 | 1 | 0.000083 |
| 16.84 | 1 | 0.000083 |
| 2957.04 | 1 | 0.000083 |
| 115.44 | 1 | 0.000083 |
| 1139.52 | 1 | 0.000083 |
| 73.20 | 1 | 0.000083 |
| 492.84 | 1 | 0.000083 |
| 100.96 | 1 | 0.000083 |
| 175.25 | 1 | 0.000083 |
| 150.24 | 1 | 0.000083 |
| 1033.76 | 1 | 0.000083 |
| 690.00 | 1 | 0.000083 |
| 36.48 | 1 | 0.000083 |
| 7224.00 | 1 | 0.000083 |
| 0.60 | 1 | 0.000083 |
| 1201.80 | 1 | 0.000083 |
| 183.00 | 1 | 0.000083 |
| 24156.00 | 1 | 0.000083 |
| 120.36 | 1 | 0.000083 |
| 999.96 | 1 | 0.000083 |
| 36.07 | 1 | 0.000083 |
| 2199.72 | 1 | 0.000083 |
| 151.44 | 1 | 0.000083 |
| 292.80 | 1 | 0.000083 |
| 242.50 | 1 | 0.000083 |
| 1002.00 | 1 | 0.000083 |
| 405.96 | 1 | 0.000083 |
| 34.64 | 1 | 0.000083 |
| 7260.00 | 1 | 0.000083 |
| 24.02 | 1 | 0.000083 |
| 295.68 | 1 | 0.000083 |
| 60.60 | 1 | 0.000083 |
| 300.84 | 1 | 0.000083 |
| 125.34 | 1 | 0.000083 |
| 217.08 | 1 | 0.000083 |
| 128.02 | 1 | 0.000083 |
| 144.60 | 1 | 0.000083 |
| 2253.80 | 1 | 0.000083 |
| 291.96 | 1 | 0.000083 |
| 132.22 | 1 | 0.000083 |
| 7596.00 | 1 | 0.000083 |
| 193.00 | 1 | 0.000083 |
| 264.44 | 1 | 0.000083 |
| 5071.76 | 1 | 0.000083 |
| 405.00 | 1 | 0.000083 |
| 277.00 | 1 | 0.000083 |
| 4.80 | 1 | 0.000083 |
| 201.60 | 1 | 0.000083 |
| 2004.00 | 1 | 0.000083 |
| 362.00 | 1 | 0.000083 |
| 138.15 | 1 | 0.000083 |
| 1536.00 | 1 | 0.000083 |
| 18.06 | 1 | 0.000083 |
| 1872.00 | 1 | 0.000083 |
| 120000.00 | 1 | 0.000083 |
| 25.60 | 1 | 0.000083 |
| 64.80 | 1 | 0.000083 |
| 44.84 | 1 | 0.000083 |
| 7992.00 | 1 | 0.000083 |
| 2184.00 | 1 | 0.000083 |
| 241.00 | 1 | 0.000083 |
| 3480.00 | 1 | 0.000083 |
| 171.00 | 1 | 0.000083 |
| 280.36 | 1 | 0.000083 |
| 108.36 | 1 | 0.000083 |
| 67.20 | 1 | 0.000083 |
| 86.56 | 1 | 0.000083 |
| 145.20 | 1 | 0.000083 |
| 1.50 | 1 | 0.000083 |
| 120.08 | 1 | 0.000083 |
| 102.72 | 1 | 0.000083 |
| 632.00 | 1 | 0.000083 |
| 131.00 | 1 | 0.000083 |
| 100.20 | 1 | 0.000083 |
| 388.92 | 1 | 0.000083 |
| 36060.00 | 1 | 0.000083 |
| 20580.00 | 1 | 0.000083 |
| 209.00 | 1 | 0.000083 |
| 384.64 | 1 | 0.000083 |
| 59.02 | 1 | 0.000083 |
| 3612.00 | 1 | 0.000083 |
| 360.24 | 1 | 0.000083 |
| 38.85 | 1 | 0.000083 |
| 9600.00 | 1 | 0.000083 |
| 10404.00 | 1 | 0.000083 |
| 515.28 | 1 | 0.000083 |
| 119.88 | 1 | 0.000083 |
| 519.60 | 1 | 0.000083 |
| 3096.00 | 1 | 0.000083 |
| 1812.00 | 1 | 0.000083 |
| 1084.32 | 1 | 0.000083 |
| 24456.00 | 1 | 0.000083 |
| 9000.00 | 1 | 0.000083 |
| 33.60 | 1 | 0.000083 |
| 10800.00 | 1 | 0.000083 |
| 25140.00 | 1 | 0.000083 |
| 89.25 | 1 | 0.000083 |
| 83.33 | 1 | 0.000083 |
| 90.96 | 1 | 0.000083 |
| 2064.00 | 1 | 0.000083 |
| 143.00 | 1 | 0.000083 |
| 71.96 | 1 | 0.000083 |
| 209.16 | 1 | 0.000083 |
| 796.00 | 1 | 0.000083 |
| 590.00 | 1 | 0.000083 |
| 225.35 | 1 | 0.000083 |
| 556.00 | 1 | 0.000083 |
| 65.86 | 1 | 0.000083 |
| 550.75 | 1 | 0.000083 |
| 360.12 | 1 | 0.000083 |
| 59.50 | 1 | 0.000083 |
| 90.10 | 1 | 0.000083 |
| 43.28 | 1 | 0.000083 |
| 50.48 | 1 | 0.000083 |
| 824.00 | 1 | 0.000083 |
| 64.90 | 1 | 0.000083 |
| 930.00 | 1 | 0.000083 |
| 8166.00 | 1 | 0.000083 |
| 150.15 | 1 | 0.000083 |
| 623.52 | 1 | 0.000083 |
| 10080.00 | 1 | 0.000083 |
| 242.00 | 1 | 0.000083 |
| 1524.00 | 1 | 0.000083 |
| 189.36 | 1 | 0.000083 |
| 16.80 | 1 | 0.000083 |
| 76.20 | 1 | 0.000083 |
| 558.96 | 1 | 0.000083 |
| 333.60 | 1 | 0.000083 |
| 710.00 | 1 | 0.000083 |
| 79992.00 | 1 | 0.000083 |
| 193.92 | 1 | 0.000083 |
| 2282.40 | 1 | 0.000083 |
| 15.52 | 1 | 0.000083 |
| 594.48 | 1 | 0.000083 |
| 105.60 | 1 | 0.000083 |
| 10.80 | 1 | 0.000083 |
| 108.96 | 1 | 0.000083 |
| 330.56 | 1 | 0.000083 |
| 166.56 | 1 | 0.000083 |
| 80.04 | 1 | 0.000083 |
| 9.24 | 1 | 0.000083 |
| 30.03 | 1 | 0.000083 |
| 92.32 | 1 | 0.000083 |
| 1692.00 | 1 | 0.000083 |
| 219.60 | 1 | 0.000083 |
| 95.05 | 1 | 0.000083 |
| 376.80 | 1 | 0.000083 |
| 399.60 | 1 | 0.000083 |
| 112.99 | 1 | 0.000083 |
| 1211.64 | 1 | 0.000083 |
| 15308.88 | 1 | 0.000083 |
| 72.20 | 1 | 0.000083 |
| 52.88 | 1 | 0.000083 |
| 5.87 | 1 | 0.000083 |
| 30050.60 | 1 | 0.000083 |
| 386.00 | 1 | 0.000083 |
| 66660.00 | 1 | 0.000083 |
| 2000000.00 | 1 | 0.000083 |
| 333.76 | 1 | 0.000083 |
| 141.00 | 1 | 0.000083 |
| 21636.00 | 1 | 0.000083 |
| 72.30 | 1 | 0.000083 |
| 288.49 | 1 | 0.000083 |
| 1060.00 | 1 | 0.000083 |
| 1728.00 | 1 | 0.000083 |
| 360.72 | 1 | 0.000083 |
| 462.00 | 1 | 0.000083 |
| 9015.24 | 1 | 0.000083 |
| 15000.00 | 1 | 0.000083 |
| 1146.72 | 1 | 0.000083 |
| 300.01 | 1 | 0.000083 |
| 1502.53 | 1 | 0.000083 |
| 1476.00 | 1 | 0.000083 |
| 36420.00 | 1 | 0.000083 |
| 624.24 | 1 | 0.000083 |
| 14400.00 | 1 | 0.000083 |
| 411.96 | 1 | 0.000083 |
| 195.96 | 1 | 0.000083 |
| 252.43 | 1 | 0.000083 |
| 143.64 | 1 | 0.000083 |
| 1150.00 | 1 | 0.000083 |
| 26.36 | 1 | 0.000083 |
| 13.82 | 1 | 0.000083 |
| 36.80 | 1 | 0.000083 |
| 68.43 | 1 | 0.000083 |
| 132.12 | 1 | 0.000083 |
# Vamos a realizar analisis por cada variable
var = "msf_cancelationdate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_cancelationdate__c es 483006. Lo que supone un 40.3107309504952% El nº de vacios para la variable msf_cancelationdate__c es 0. Lo que supone un 0.0%
['isdeleted', 'msf_cancelationdate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2014-03-13 | 2887 | 0.403663 |
| 2020-03-12 | 2637 | 0.368708 |
| 2018-03-07 | 2198 | 0.307326 |
| 2018-04-09 | 1957 | 0.273629 |
| 2023-05-10 | 1867 | 0.261045 |
| ... | ... | ... |
| 2002-08-07 | 1 | 0.000140 |
| 2012-06-23 | 1 | 0.000140 |
| 2008-03-21 | 1 | 0.000140 |
| 2007-02-17 | 1 | 0.000140 |
| 2002-07-04 | 1 | 0.000140 |
7160 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_cancelationreason__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_cancelationreason__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_cancelationreason__c es 483501. Lo que supone un 40.35204267710004%
['isdeleted', 'msf_cancelationdate__c', 'msf_cancelationreason__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 483501 | 40.352043 | |
| Impago aportaciones | 285429 | 23.821343 |
| Unpaid | 102935 | 8.590753 |
| Baja económica argumentada | 66594 | 5.557804 |
| 3 Obs/Tcs Devueltas | 66286 | 5.532099 |
| Económico | 39385 | 3.286995 |
| N/S | 35770 | 2.985294 |
| Razones personales | 34347 | 2.866533 |
| Voluntary withdrawal | 16894 | 1.409940 |
| Deceased | 15218 | 1.270064 |
| Otras razones | 8112 | 0.677012 |
| 2 OBS devueltas es la primera de su historia | 5346 | 0.446167 |
| Baja argumentada | 4854 | 0.405105 |
| Baja por cambio de titular | 3394 | 0.283257 |
| Cambio de domicilio | 3241 | 0.270487 |
| Colabora con otra ONG | 3065 | 0.255799 |
| Baja e llamada de Bienvenida | 2933 | 0.244782 |
| Baja por impago orden Socio | 2832 | 0.236353 |
| 1 OBS/TCS devueltas con otras aportaciones impagadas en los últimos 2 años | 2711 | 0.226255 |
| Baja en llamada de gestión de devoluciones | 1827 | 0.152478 |
| Tarjeta crédito caducada | 1799 | 0.150141 |
| Baja proactiva Coronavirus | 1711 | 0.142797 |
| Decepcionado | 1402 | 0.117008 |
| Baja en llamada de aumentos | 1328 | 0.110832 |
| Errores administrativos MSF | 1277 | 0.106576 |
| Incidencias Web | 906 | 0.075613 |
| Cierre/Cambio cuenta bancaria | 767 | 0.064012 |
| Pruebas | 650 | 0.054248 |
| Impago Coronavirus | 464 | 0.038725 |
| LOPD | 449 | 0.037473 |
| Desacuerdo política intervención | 439 | 0.036638 |
| Captador F2F-TLMK me informó mal | 341 | 0.028459 |
| Baja socio temporal | 323 | 0.026957 |
| Error del proveedor | 307 | 0.025622 |
| Desacuerdo aborto | 252 | 0.021031 |
| Duplicado | 238 | 0.019863 |
| Desacuerdo política captacion de fondos | 229 | 0.019112 |
| Baja reactiva Coronavirus | 214 | 0.017860 |
| Desacuerdo operaciones mediterraneo | 142 | 0.011851 |
| 2 TCS devueltas | 116 | 0.009681 |
| Desacuerdo gestión financiera | 85 | 0.007094 |
| Desacuerdo destino ayuda | 59 | 0.004924 |
| Desacuerdo colaboración con Empresas | 26 | 0.002170 |
| LORTAD | 9 | 0.000751 |
# Vamos a realizar analisis por cada variable
var = "msf_currentcampaign__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_currentcampaign__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_currentcampaign__c es 19006. Lo que supone un 1.5862033855585889%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mqtMQAQ | 95523 | 7.972162 |
| 7013Y000001mqtnQAA | 47771 | 3.986874 |
| 7013Y000001vXGdQAM | 30352 | 2.533118 |
| 7013Y000001mrBLQAY | 30029 | 2.506161 |
| 7013Y000001mrCzQAI | 28901 | 2.412021 |
| ... | ... | ... |
| 7013Y000001mrkDQAQ | 1 | 0.000083 |
| 7013Y000001mrGNQAY | 1 | 0.000083 |
| 7013Y000001mrO8QAI | 1 | 0.000083 |
| 7013Y000001Mc2FQAS | 1 | 0.000083 |
| 7013Y000001mr7eQAA | 1 | 0.000083 |
2600 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_currentleadsource1__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_currentleadsource1__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_currentleadsource1__c es 19170. Lo que supone un 1.5998905030599888%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Telemarketing | 509871 | 42.552831 |
| Persona a persona | 255845 | 21.352321 |
| Cupón | 84961 | 7.090678 |
| Otro | 71618 | 5.977097 |
| Personal con tablet | 64333 | 5.369106 |
| Web MSF | 63379 | 5.289487 |
| Teléfono campaña | 48761 | 4.069497 |
| Teléfono SAS | 21594 | 1.802193 |
| Web campaña | 20290 | 1.693364 |
| 19170 | 1.599891 | |
| Entidad financiera | 8710 | 0.726919 |
| Email a SAS | 8083 | 0.674591 |
| Web terceros | 7062 | 0.589381 |
| Teléfono web | 7001 | 0.584290 |
| Correo postal sin cupón | 4185 | 0.349272 |
| Web MSF Mi perfil | 1057 | 0.088215 |
| Cloud page | 885 | 0.073860 |
| Eventos | 763 | 0.063678 |
| Teléfono Officers | 389 | 0.032465 |
| Email a Empresas | 182 | 0.015189 |
| Email a officers Mid Donors | 22 | 0.001836 |
| SMS | 15 | 0.001252 |
| Email a One to one | 8 | 0.000668 |
| n/a | 7 | 0.000584 |
| Email a Bodas | 6 | 0.000501 |
| Teléfono Herencias y Legados | 5 | 0.000417 |
| Email a Iniciativas Solidarias | 2 | 0.000167 |
| Email Director/a General | 2 | 0.000167 |
| Redes Sociales | 1 | 0.000083 |
# Vamos a realizar analisis por cada variable
var = "msf_leadsource1__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable msf_leadsource1__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_leadsource1__c es 173. Lo que supone un 0.014438239803306108%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Persona a persona | 374964 | 31.293758 |
| Telemarketing | 289168 | 24.133393 |
| Otro | 136249 | 11.371074 |
| Cupón | 133197 | 11.116360 |
| Web MSF | 76674 | 6.399061 |
| Personal con tablet | 74059 | 6.180819 |
| Teléfono campaña | 61869 | 5.163465 |
| Web campaña | 14210 | 1.185939 |
| Web terceros | 12698 | 1.059750 |
| Teléfono SAS | 6595 | 0.550406 |
| Teléfono web | 5748 | 0.479717 |
| Email a SAS | 5354 | 0.446834 |
| Correo postal sin cupón | 5008 | 0.417958 |
| Eventos | 1577 | 0.131613 |
| Entidad financiera | 343 | 0.028626 |
| 173 | 0.014438 | |
| Email a Empresas | 162 | 0.013520 |
| Teléfono Officers | 126 | 0.010516 |
| Email a officers Mid Donors | 9 | 0.000751 |
| Cloud page | 8 | 0.000668 |
| Email a One to one | 5 | 0.000417 |
| n/a | 3 | 0.000250 |
| SMS | 2 | 0.000167 |
| Teléfono Herencias y Legados | 2 | 0.000167 |
| Email a Iniciativas Solidarias | 2 | 0.000167 |
| Redes Sociales | 1 | 0.000083 |
| Email Director/a General | 1 | 0.000083 |
# Vamos a realizar analisis por cada variable
var = "npe03__amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__amount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npe03__amount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 10.00 | 283542 | 23.663858 |
| 15.00 | 133048 | 11.103924 |
| 5.00 | 113650 | 9.485006 |
| 20.00 | 89248 | 7.448463 |
| 12.00 | 62605 | 5.224890 |
| 30.00 | 50617 | 4.224395 |
| 6.00 | 48472 | 4.045378 |
| 3.00 | 30398 | 2.536957 |
| 25.00 | 29725 | 2.480790 |
| 50.00 | 24828 | 2.072096 |
| 8.00 | 22023 | 1.837996 |
| 60.00 | 19257 | 1.607151 |
| 0.00 | 19245 | 1.606150 |
| 7.00 | 16418 | 1.370214 |
| 6.01 | 16086 | 1.342506 |
| 100.00 | 14742 | 1.230338 |
| 14.00 | 13508 | 1.127351 |
| 40.00 | 12300 | 1.026534 |
| 30.05 | 12251 | 1.022444 |
| 18.03 | 11212 | 0.935731 |
| 18.00 | 8598 | 0.717572 |
| 17.00 | 7785 | 0.649721 |
| 60.10 | 7704 | 0.642961 |
| 4.33 | 6937 | 0.578948 |
| 35.00 | 6936 | 0.578865 |
| 9.00 | 6883 | 0.574442 |
| 150.00 | 6295 | 0.525368 |
| 4.00 | 6150 | 0.513267 |
| 120.00 | 6065 | 0.506173 |
| 11.00 | 5674 | 0.473541 |
| 16.00 | 5324 | 0.444331 |
| 13.00 | 5227 | 0.436235 |
| 22.00 | 4576 | 0.381904 |
| 12.02 | 4428 | 0.369552 |
| 26.00 | 4239 | 0.353779 |
| 45.00 | 4037 | 0.336920 |
| 200.00 | 3959 | 0.330410 |
| 36.00 | 3453 | 0.288181 |
| 19.00 | 3367 | 0.281003 |
| 70.00 | 3218 | 0.268568 |
| 75.00 | 2988 | 0.249373 |
| 80.00 | 2808 | 0.234350 |
| 90.00 | 2800 | 0.233682 |
| 23.00 | 2715 | 0.226589 |
| 24.00 | 2615 | 0.218243 |
| 300.00 | 1999 | 0.166833 |
| 32.00 | 1809 | 0.150976 |
| 21.00 | 1628 | 0.135870 |
| 90.15 | 1614 | 0.134701 |
| 28.00 | 1449 | 0.120931 |
| 250.00 | 1391 | 0.116090 |
| 65.00 | 1357 | 0.113253 |
| 55.00 | 1308 | 0.109163 |
| 27.00 | 1303 | 0.108746 |
| 72.12 | 1253 | 0.104573 |
| 42.00 | 1185 | 0.098898 |
| 33.00 | 1124 | 0.093807 |
| 34.86 | 1123 | 0.093723 |
| 72.00 | 1114 | 0.092972 |
| 180.00 | 1069 | 0.089217 |
| 125.00 | 919 | 0.076698 |
| 130.00 | 910 | 0.075947 |
| 36.06 | 865 | 0.072191 |
| 110.00 | 813 | 0.067851 |
| 140.00 | 711 | 0.059339 |
| 120.20 | 653 | 0.054498 |
| 150.25 | 636 | 0.053079 |
| 500.00 | 601 | 0.050158 |
| 9.02 | 594 | 0.049574 |
| 14.42 | 586 | 0.048906 |
| 48.00 | 577 | 0.048155 |
| 160.00 | 557 | 0.046486 |
| 52.00 | 555 | 0.046319 |
| 3.01 | 551 | 0.045985 |
| 38.00 | 493 | 0.041145 |
| 85.00 | 489 | 0.040811 |
| 34.00 | 485 | 0.040477 |
| 400.00 | 456 | 0.038057 |
| 240.00 | 441 | 0.036805 |
| 37.00 | 440 | 0.036722 |
| 24.04 | 424 | 0.035386 |
| 7.50 | 416 | 0.034719 |
| 15.03 | 408 | 0.034051 |
| 48.08 | 397 | 0.033133 |
| 84.00 | 383 | 0.031964 |
| 170.00 | 379 | 0.031631 |
| 31.00 | 376 | 0.031380 |
| 8.67 | 357 | 0.029795 |
| 175.00 | 355 | 0.029628 |
| 260.00 | 335 | 0.027958 |
| 350.00 | 329 | 0.027458 |
| 600.00 | 322 | 0.026873 |
| 1000.00 | 313 | 0.026122 |
| 210.00 | 283 | 0.023619 |
| 28.85 | 271 | 0.022617 |
| 29.00 | 269 | 0.022450 |
| 165.00 | 260 | 0.021699 |
| 105.00 | 230 | 0.019195 |
| 115.00 | 227 | 0.018945 |
| 220.00 | 226 | 0.018862 |
| 46.00 | 219 | 0.018277 |
| 93.16 | 218 | 0.018194 |
| 62.00 | 203 | 0.016942 |
| 180.30 | 202 | 0.016859 |
| 12.50 | 201 | 0.016775 |
| 230.00 | 196 | 0.016358 |
| 56.00 | 189 | 0.015774 |
| 8.66 | 187 | 0.015607 |
| 78.00 | 186 | 0.015523 |
| 6.02 | 184 | 0.015356 |
| 135.00 | 184 | 0.015356 |
| 43.00 | 183 | 0.015273 |
| 144.00 | 181 | 0.015106 |
| 39.00 | 180 | 0.015022 |
| 44.00 | 179 | 0.014939 |
| 95.00 | 175 | 0.014605 |
| 54.00 | 174 | 0.014522 |
| 42.07 | 155 | 0.012936 |
| 41.00 | 153 | 0.012769 |
| 300.51 | 150 | 0.012519 |
| 34.85 | 150 | 0.012519 |
| 225.00 | 144 | 0.012018 |
| 66.00 | 144 | 0.012018 |
| 360.00 | 142 | 0.011851 |
| 190.00 | 138 | 0.011517 |
| 54.09 | 126 | 0.010516 |
| 96.00 | 124 | 0.010349 |
| 58.00 | 115 | 0.009598 |
| 155.00 | 114 | 0.009514 |
| 450.00 | 109 | 0.009097 |
| 9.01 | 106 | 0.008847 |
| 63.00 | 95 | 0.007929 |
| 2.00 | 95 | 0.007929 |
| 270.00 | 87 | 0.007261 |
| 57.70 | 87 | 0.007261 |
| 15.02 | 86 | 0.007177 |
| 47.00 | 83 | 0.006927 |
| 53.00 | 79 | 0.006593 |
| 51.00 | 77 | 0.006426 |
| 84.14 | 76 | 0.006343 |
| 320.00 | 73 | 0.006092 |
| 275.00 | 70 | 0.005842 |
| 601.01 | 65 | 0.005425 |
| 30.12 | 65 | 0.005425 |
| 144.24 | 65 | 0.005425 |
| 145.00 | 65 | 0.005425 |
| 280.00 | 62 | 0.005174 |
| 68.00 | 61 | 0.005091 |
| 310.00 | 60 | 0.005007 |
| 74.00 | 59 | 0.004924 |
| 98.00 | 59 | 0.004924 |
| 700.00 | 57 | 0.004757 |
| 57.00 | 56 | 0.004674 |
| 390.00 | 56 | 0.004674 |
| 300.50 | 55 | 0.004590 |
| 82.00 | 54 | 0.004507 |
| 330.00 | 54 | 0.004507 |
| 4.50 | 53 | 0.004423 |
| 1.00 | 51 | 0.004256 |
| 67.00 | 49 | 0.004089 |
| 112.00 | 47 | 0.003923 |
| 800.00 | 47 | 0.003923 |
| 185.00 | 46 | 0.003839 |
| 325.00 | 45 | 0.003756 |
| 3.50 | 44 | 0.003672 |
| 93.15 | 44 | 0.003672 |
| 550.00 | 43 | 0.003589 |
| 91.00 | 42 | 0.003505 |
| 108.18 | 41 | 0.003422 |
| 37.50 | 40 | 0.003338 |
| 2000.00 | 40 | 0.003338 |
| 1500.00 | 38 | 0.003171 |
| 76.00 | 38 | 0.003171 |
| 375.00 | 38 | 0.003171 |
| 108.00 | 36 | 0.003004 |
| 124.00 | 36 | 0.003004 |
| 1200.00 | 36 | 0.003004 |
| 21.04 | 35 | 0.002921 |
| 61.00 | 35 | 0.002921 |
| 64.00 | 33 | 0.002754 |
| 6.33 | 32 | 0.002671 |
| 7.05 | 32 | 0.002671 |
| 86.00 | 31 | 0.002587 |
| 240.41 | 31 | 0.002587 |
| 45.08 | 31 | 0.002587 |
| 92.00 | 31 | 0.002587 |
| 73.00 | 31 | 0.002587 |
| 94.00 | 31 | 0.002587 |
| 132.00 | 30 | 0.002504 |
| 77.00 | 30 | 0.002504 |
| 5.33 | 29 | 0.002420 |
| 365.00 | 29 | 0.002420 |
| 290.00 | 28 | 0.002337 |
| 156.00 | 28 | 0.002337 |
| 60.24 | 27 | 0.002253 |
| 59.00 | 26 | 0.002170 |
| 78.13 | 26 | 0.002170 |
| 102.00 | 25 | 0.002086 |
| 81.00 | 25 | 0.002086 |
| 162.00 | 25 | 0.002086 |
| 650.00 | 24 | 0.002003 |
| 520.00 | 24 | 0.002003 |
| 49.00 | 24 | 0.002003 |
| 7.21 | 23 | 0.001920 |
| 370.00 | 23 | 0.001920 |
| 28.84 | 23 | 0.001920 |
| 420.00 | 23 | 0.001920 |
| 27.05 | 22 | 0.001836 |
| 235.00 | 21 | 0.001753 |
| 900.00 | 21 | 0.001753 |
| 215.00 | 21 | 0.001753 |
| 45.07 | 21 | 0.001753 |
| 87.00 | 21 | 0.001753 |
| 205.00 | 20 | 0.001669 |
| 1.80 | 20 | 0.001669 |
| 9.12 | 20 | 0.001669 |
| 71.00 | 19 | 0.001586 |
| 168.00 | 19 | 0.001586 |
| 750.00 | 19 | 0.001586 |
| 210.35 | 18 | 0.001502 |
| 83.00 | 18 | 0.001502 |
| 22.50 | 18 | 0.001502 |
| 195.00 | 18 | 0.001502 |
| 126.00 | 18 | 0.001502 |
| 104.00 | 17 | 0.001419 |
| 69.00 | 17 | 0.001419 |
| 380.00 | 17 | 0.001419 |
| 425.00 | 17 | 0.001419 |
| 340.00 | 17 | 0.001419 |
| 122.00 | 17 | 0.001419 |
| 216.00 | 16 | 0.001335 |
| 101.00 | 16 | 0.001335 |
| 8.50 | 16 | 0.001335 |
| 3000.00 | 16 | 0.001335 |
| 360.61 | 16 | 0.001335 |
| 88.00 | 15 | 0.001252 |
| 216.36 | 15 | 0.001252 |
| 15.20 | 14 | 0.001168 |
| 460.00 | 14 | 0.001168 |
| 152.00 | 14 | 0.001168 |
| 106.00 | 14 | 0.001168 |
| 136.00 | 14 | 0.001168 |
| 240.40 | 14 | 0.001168 |
| 148.00 | 13 | 0.001085 |
| 433.00 | 13 | 0.001085 |
| 174.00 | 13 | 0.001085 |
| 480.00 | 13 | 0.001085 |
| 10.01 | 12 | 0.001001 |
| 66.11 | 12 | 0.001001 |
| 3.61 | 12 | 0.001001 |
| 6.50 | 12 | 0.001001 |
| 5.50 | 11 | 0.000918 |
| 0.60 | 11 | 0.000918 |
| 116.00 | 11 | 0.000918 |
| 93.00 | 11 | 0.000918 |
| 255.00 | 11 | 0.000918 |
| 90.36 | 11 | 0.000918 |
| 21.03 | 10 | 0.000835 |
| 97.00 | 10 | 0.000835 |
| 224.00 | 10 | 0.000835 |
| 7.51 | 10 | 0.000835 |
| 79.00 | 10 | 0.000835 |
| 51.96 | 9 | 0.000751 |
| 360.60 | 9 | 0.000751 |
| 10.50 | 9 | 0.000751 |
| 123.00 | 9 | 0.000751 |
| 410.00 | 9 | 0.000751 |
| 33.05 | 8 | 0.000668 |
| 142.00 | 8 | 0.000668 |
| 470.00 | 8 | 0.000668 |
| 1.20 | 8 | 0.000668 |
| 99.00 | 8 | 0.000668 |
| 14.33 | 8 | 0.000668 |
| 52.89 | 8 | 0.000668 |
| 17.50 | 7 | 0.000584 |
| 182.00 | 7 | 0.000584 |
| 236.00 | 7 | 0.000584 |
| 32.50 | 7 | 0.000584 |
| 57.69 | 7 | 0.000584 |
| 2.40 | 7 | 0.000584 |
| 430.00 | 7 | 0.000584 |
| 202.00 | 7 | 0.000584 |
| 1250.00 | 7 | 0.000584 |
| 96.16 | 7 | 0.000584 |
| 4.21 | 7 | 0.000584 |
| 40.05 | 7 | 0.000584 |
| 285.00 | 6 | 0.000501 |
| 103.00 | 6 | 0.000501 |
| 35.05 | 6 | 0.000501 |
| 850.00 | 6 | 0.000501 |
| 134.00 | 6 | 0.000501 |
| 315.00 | 6 | 0.000501 |
| 620.00 | 6 | 0.000501 |
| 6000.00 | 6 | 0.000501 |
| 27.04 | 6 | 0.000501 |
| 13.50 | 6 | 0.000501 |
| 4.81 | 6 | 0.000501 |
| 8.01 | 6 | 0.000501 |
| 721.22 | 6 | 0.000501 |
| 204.00 | 6 | 0.000501 |
| 192.00 | 6 | 0.000501 |
| 151.00 | 6 | 0.000501 |
| 660.00 | 6 | 0.000501 |
| 8.33 | 6 | 0.000501 |
| 1.50 | 6 | 0.000501 |
| 640.00 | 6 | 0.000501 |
| 196.00 | 6 | 0.000501 |
| 324.00 | 5 | 0.000417 |
| 107.00 | 5 | 0.000417 |
| 245.00 | 5 | 0.000417 |
| 265.00 | 5 | 0.000417 |
| 720.00 | 5 | 0.000417 |
| 164.00 | 5 | 0.000417 |
| 440.00 | 5 | 0.000417 |
| 114.00 | 5 | 0.000417 |
| 6.61 | 5 | 0.000417 |
| 109.00 | 5 | 0.000417 |
| 286.00 | 5 | 0.000417 |
| 112.99 | 5 | 0.000417 |
| 305.00 | 5 | 0.000417 |
| 128.00 | 5 | 0.000417 |
| 75.13 | 5 | 0.000417 |
| 222.00 | 5 | 0.000417 |
| 100.15 | 5 | 0.000417 |
| 4.20 | 5 | 0.000417 |
| 4.51 | 5 | 0.000417 |
| 1100.00 | 5 | 0.000417 |
| 5000.00 | 5 | 0.000417 |
| 18.75 | 5 | 0.000417 |
| 6.25 | 5 | 0.000417 |
| 345.00 | 4 | 0.000334 |
| 121.00 | 4 | 0.000334 |
| 3.60 | 4 | 0.000334 |
| 19.83 | 4 | 0.000334 |
| 212.00 | 4 | 0.000334 |
| 11.50 | 4 | 0.000334 |
| 450.75 | 4 | 0.000334 |
| 70.10 | 4 | 0.000334 |
| 117.00 | 4 | 0.000334 |
| 158.00 | 4 | 0.000334 |
| 137.00 | 4 | 0.000334 |
| 333.00 | 4 | 0.000334 |
| 113.00 | 4 | 0.000334 |
| 30.50 | 4 | 0.000334 |
| 198.00 | 4 | 0.000334 |
| 7.81 | 4 | 0.000334 |
| 475.00 | 4 | 0.000334 |
| 89.00 | 4 | 0.000334 |
| 21.64 | 4 | 0.000334 |
| 154.00 | 4 | 0.000334 |
| 2.50 | 4 | 0.000334 |
| 20.03 | 4 | 0.000334 |
| 194.00 | 4 | 0.000334 |
| 118.00 | 4 | 0.000334 |
| 7.01 | 4 | 0.000334 |
| 625.00 | 4 | 0.000334 |
| 201.00 | 4 | 0.000334 |
| 149.00 | 4 | 0.000334 |
| 161.00 | 4 | 0.000334 |
| 18.06 | 4 | 0.000334 |
| 184.00 | 4 | 0.000334 |
| 312.00 | 4 | 0.000334 |
| 270.46 | 4 | 0.000334 |
| 22.53 | 3 | 0.000250 |
| 901.52 | 3 | 0.000250 |
| 111.00 | 3 | 0.000250 |
| 166.00 | 3 | 0.000250 |
| 187.00 | 3 | 0.000250 |
| 62.50 | 3 | 0.000250 |
| 540.00 | 3 | 0.000250 |
| 51.09 | 3 | 0.000250 |
| 264.00 | 3 | 0.000250 |
| 65.10 | 3 | 0.000250 |
| 725.00 | 3 | 0.000250 |
| 60.01 | 3 | 0.000250 |
| 480.81 | 3 | 0.000250 |
| 139.00 | 3 | 0.000250 |
| 244.00 | 3 | 0.000250 |
| 186.00 | 3 | 0.000250 |
| 198.33 | 3 | 0.000250 |
| 274.00 | 3 | 0.000250 |
| 94.96 | 3 | 0.000250 |
| 153.00 | 3 | 0.000250 |
| 172.00 | 3 | 0.000250 |
| 131.00 | 3 | 0.000250 |
| 133.00 | 3 | 0.000250 |
| 820.00 | 3 | 0.000250 |
| 450.76 | 3 | 0.000250 |
| 0.06 | 3 | 0.000250 |
| 167.00 | 3 | 0.000250 |
| 560.00 | 3 | 0.000250 |
| 576.00 | 3 | 0.000250 |
| 127.00 | 3 | 0.000250 |
| 580.00 | 3 | 0.000250 |
| 415.00 | 3 | 0.000250 |
| 7.33 | 3 | 0.000250 |
| 7.77 | 2 | 0.000167 |
| 232.00 | 2 | 0.000167 |
| 9.20 | 2 | 0.000167 |
| 45.05 | 2 | 0.000167 |
| 16.67 | 2 | 0.000167 |
| 141.00 | 2 | 0.000167 |
| 10.22 | 2 | 0.000167 |
| 120.01 | 2 | 0.000167 |
| 27.50 | 2 | 0.000167 |
| 13.22 | 2 | 0.000167 |
| 416.00 | 2 | 0.000167 |
| 173.00 | 2 | 0.000167 |
| 6.31 | 2 | 0.000167 |
| 43.27 | 2 | 0.000167 |
| 301.00 | 2 | 0.000167 |
| 19.50 | 2 | 0.000167 |
| 2500.00 | 2 | 0.000167 |
| 52.50 | 2 | 0.000167 |
| 16.50 | 2 | 0.000167 |
| 160.25 | 2 | 0.000167 |
| 3.33 | 2 | 0.000167 |
| 4.66 | 2 | 0.000167 |
| 36.66 | 2 | 0.000167 |
| 138.00 | 2 | 0.000167 |
| 242.00 | 2 | 0.000167 |
| 6.66 | 2 | 0.000167 |
| 14.02 | 2 | 0.000167 |
| 12.99 | 2 | 0.000167 |
| 12.62 | 2 | 0.000167 |
| 32.05 | 2 | 0.000167 |
| 252.00 | 2 | 0.000167 |
| 177.00 | 2 | 0.000167 |
| 157.00 | 2 | 0.000167 |
| 234.00 | 2 | 0.000167 |
| 189.00 | 2 | 0.000167 |
| 12.04 | 2 | 0.000167 |
| 570.00 | 2 | 0.000167 |
| 102.17 | 2 | 0.000167 |
| 5.41 | 2 | 0.000167 |
| 37.06 | 2 | 0.000167 |
| 288.00 | 2 | 0.000167 |
| 25.50 | 2 | 0.000167 |
| 585.00 | 2 | 0.000167 |
| 384.00 | 2 | 0.000167 |
| 20.05 | 2 | 0.000167 |
| 112.50 | 2 | 0.000167 |
| 295.00 | 2 | 0.000167 |
| 7212.00 | 2 | 0.000167 |
| 16.53 | 2 | 0.000167 |
| 10.33 | 2 | 0.000167 |
| 14.77 | 2 | 0.000167 |
| 740.00 | 2 | 0.000167 |
| 258.00 | 2 | 0.000167 |
| 18.60 | 2 | 0.000167 |
| 248.00 | 2 | 0.000167 |
| 138.23 | 2 | 0.000167 |
| 83.33 | 2 | 0.000167 |
| 30.02 | 2 | 0.000167 |
| 10.82 | 2 | 0.000167 |
| 335.00 | 2 | 0.000167 |
| 128.02 | 2 | 0.000167 |
| 25.24 | 2 | 0.000167 |
| 8000.00 | 2 | 0.000167 |
| 42.05 | 2 | 0.000167 |
| 2.10 | 2 | 0.000167 |
| 132.22 | 2 | 0.000167 |
| 193.00 | 2 | 0.000167 |
| 302.00 | 2 | 0.000167 |
| 405.00 | 2 | 0.000167 |
| 39.07 | 2 | 0.000167 |
| 24.50 | 2 | 0.000167 |
| 13.82 | 2 | 0.000167 |
| 100.01 | 2 | 0.000167 |
| 15.60 | 2 | 0.000167 |
| 1502.53 | 2 | 0.000167 |
| 13.33 | 2 | 0.000167 |
| 20.83 | 2 | 0.000167 |
| 602.00 | 2 | 0.000167 |
| 16.80 | 2 | 0.000167 |
| 346.00 | 2 | 0.000167 |
| 420.71 | 2 | 0.000167 |
| 12.01 | 2 | 0.000167 |
| 126.21 | 2 | 0.000167 |
| 3600.00 | 2 | 0.000167 |
| 23.03 | 2 | 0.000167 |
| 36.05 | 2 | 0.000167 |
| 4.30 | 2 | 0.000167 |
| 18.63 | 2 | 0.000167 |
| 13.52 | 2 | 0.000167 |
| 143.00 | 2 | 0.000167 |
| 60.05 | 2 | 0.000167 |
| 8.30 | 2 | 0.000167 |
| 123.21 | 2 | 0.000167 |
| 1202.02 | 2 | 0.000167 |
| 525.00 | 2 | 0.000167 |
| 18.66 | 2 | 0.000167 |
| 39.85 | 2 | 0.000167 |
| 20.01 | 2 | 0.000167 |
| 446.00 | 2 | 0.000167 |
| 70.25 | 1 | 0.000083 |
| 75.12 | 1 | 0.000083 |
| 4.99 | 1 | 0.000083 |
| 8.41 | 1 | 0.000083 |
| 13.70 | 1 | 0.000083 |
| 13.34 | 1 | 0.000083 |
| 21.50 | 1 | 0.000083 |
| 75.10 | 1 | 0.000083 |
| 19.90 | 1 | 0.000083 |
| 6.20 | 1 | 0.000083 |
| 3.20 | 1 | 0.000083 |
| 253.00 | 1 | 0.000083 |
| 39.01 | 1 | 0.000083 |
| 385.00 | 1 | 0.000083 |
| 20.33 | 1 | 0.000083 |
| 10.25 | 1 | 0.000083 |
| 13.40 | 1 | 0.000083 |
| 77.10 | 1 | 0.000083 |
| 31.84 | 1 | 0.000083 |
| 40.06 | 1 | 0.000083 |
| 323.00 | 1 | 0.000083 |
| 22.02 | 1 | 0.000083 |
| 181.00 | 1 | 0.000083 |
| 6.60 | 1 | 0.000083 |
| 129.00 | 1 | 0.000083 |
| 81.10 | 1 | 0.000083 |
| 308.00 | 1 | 0.000083 |
| 41.06 | 1 | 0.000083 |
| 17.34 | 1 | 0.000083 |
| 306.51 | 1 | 0.000083 |
| 70.24 | 1 | 0.000083 |
| 52.88 | 1 | 0.000083 |
| 2250.00 | 1 | 0.000083 |
| 272.00 | 1 | 0.000083 |
| 224.24 | 1 | 0.000083 |
| 90.75 | 1 | 0.000083 |
| 70.01 | 1 | 0.000083 |
| 16.60 | 1 | 0.000083 |
| 175.25 | 1 | 0.000083 |
| 63.10 | 1 | 0.000083 |
| 2.99 | 1 | 0.000083 |
| 288.48 | 1 | 0.000083 |
| 180.20 | 1 | 0.000083 |
| 40.02 | 1 | 0.000083 |
| 23.50 | 1 | 0.000083 |
| 66.10 | 1 | 0.000083 |
| 211.00 | 1 | 0.000083 |
| 112.12 | 1 | 0.000083 |
| 206.14 | 1 | 0.000083 |
| 15.67 | 1 | 0.000083 |
| 316.00 | 1 | 0.000083 |
| 125.20 | 1 | 0.000083 |
| 9.50 | 1 | 0.000083 |
| 44.06 | 1 | 0.000083 |
| 146.00 | 1 | 0.000083 |
| 278.00 | 1 | 0.000083 |
| 755.00 | 1 | 0.000083 |
| 31.05 | 1 | 0.000083 |
| 20.43 | 1 | 0.000083 |
| 504.00 | 1 | 0.000083 |
| 306.00 | 1 | 0.000083 |
| 38.06 | 1 | 0.000083 |
| 565.00 | 1 | 0.000083 |
| 120.10 | 1 | 0.000083 |
| 11.33 | 1 | 0.000083 |
| 28.34 | 1 | 0.000083 |
| 356.00 | 1 | 0.000083 |
| 450.50 | 1 | 0.000083 |
| 338.00 | 1 | 0.000083 |
| 1360.00 | 1 | 0.000083 |
| 1442.43 | 1 | 0.000083 |
| 159.00 | 1 | 0.000083 |
| 304.00 | 1 | 0.000083 |
| 322.00 | 1 | 0.000083 |
| 262.00 | 1 | 0.000083 |
| 147.00 | 1 | 0.000083 |
| 169.00 | 1 | 0.000083 |
| 7.25 | 1 | 0.000083 |
| 19.03 | 1 | 0.000083 |
| 218.00 | 1 | 0.000083 |
| 860.00 | 1 | 0.000083 |
| 318.00 | 1 | 0.000083 |
| 10.57 | 1 | 0.000083 |
| 185.25 | 1 | 0.000083 |
| 52.58 | 1 | 0.000083 |
| 66.50 | 1 | 0.000083 |
| 182.50 | 1 | 0.000083 |
| 950.00 | 1 | 0.000083 |
| 320.50 | 1 | 0.000083 |
| 56.25 | 1 | 0.000083 |
| 451.00 | 1 | 0.000083 |
| 256.00 | 1 | 0.000083 |
| 183.00 | 1 | 0.000083 |
| 136.50 | 1 | 0.000083 |
| 530.00 | 1 | 0.000083 |
| 126.15 | 1 | 0.000083 |
| 17.73 | 1 | 0.000083 |
| 1750.00 | 1 | 0.000083 |
| 505.00 | 1 | 0.000083 |
| 47.50 | 1 | 0.000083 |
| 34.84 | 1 | 0.000083 |
| 382.00 | 1 | 0.000083 |
| 60.15 | 1 | 0.000083 |
| 25000.00 | 1 | 0.000083 |
| 1120.00 | 1 | 0.000083 |
| 33.33 | 1 | 0.000083 |
| 4.63 | 1 | 0.000083 |
| 468.00 | 1 | 0.000083 |
| 252.36 | 1 | 0.000083 |
| 100.10 | 1 | 0.000083 |
| 39.99 | 1 | 0.000083 |
| 9.61 | 1 | 0.000083 |
| 14.50 | 1 | 0.000083 |
| 67.50 | 1 | 0.000083 |
| 289.00 | 1 | 0.000083 |
| 28.66 | 1 | 0.000083 |
| 374.00 | 1 | 0.000083 |
| 214.00 | 1 | 0.000083 |
| 13.02 | 1 | 0.000083 |
| 5.30 | 1 | 0.000083 |
| 34.12 | 1 | 0.000083 |
| 199.00 | 1 | 0.000083 |
| 203.00 | 1 | 0.000083 |
| 30.40 | 1 | 0.000083 |
| 311.00 | 1 | 0.000083 |
| 1803.03 | 1 | 0.000083 |
| 116.66 | 1 | 0.000083 |
| 229.00 | 1 | 0.000083 |
| 7.60 | 1 | 0.000083 |
| 675.00 | 1 | 0.000083 |
| 780.00 | 1 | 0.000083 |
| 10.05 | 1 | 0.000083 |
| 16.25 | 1 | 0.000083 |
| 119.00 | 1 | 0.000083 |
| 11.66 | 1 | 0.000083 |
| 207.00 | 1 | 0.000083 |
| 20.64 | 1 | 0.000083 |
| 55.80 | 1 | 0.000083 |
| 5.83 | 1 | 0.000083 |
| 71.96 | 1 | 0.000083 |
| 49.58 | 1 | 0.000083 |
| 9.32 | 1 | 0.000083 |
| 61.30 | 1 | 0.000083 |
| 143.04 | 1 | 0.000083 |
| 4.55 | 1 | 0.000083 |
| 0.72 | 1 | 0.000083 |
| 70.12 | 1 | 0.000083 |
| 5.25 | 1 | 0.000083 |
| 66.66 | 1 | 0.000083 |
| 60.12 | 1 | 0.000083 |
| 261.44 | 1 | 0.000083 |
| 30.95 | 1 | 0.000083 |
| 18.28 | 1 | 0.000083 |
| 16.46 | 1 | 0.000083 |
| 8.26 | 1 | 0.000083 |
| 22.54 | 1 | 0.000083 |
| 6010.12 | 1 | 0.000083 |
| 8.77 | 1 | 0.000083 |
| 9.25 | 1 | 0.000083 |
| 24.33 | 1 | 0.000083 |
| 54.93 | 1 | 0.000083 |
| 14.61 | 1 | 0.000083 |
| 1267.94 | 1 | 0.000083 |
| 6.10 | 1 | 0.000083 |
| 12.10 | 1 | 0.000083 |
| 16.83 | 1 | 0.000083 |
| 28.03 | 1 | 0.000083 |
| 15.29 | 1 | 0.000083 |
| 16.23 | 1 | 0.000083 |
| 19.23 | 1 | 0.000083 |
| 180.32 | 1 | 0.000083 |
| 3.91 | 1 | 0.000083 |
| 7.20 | 1 | 0.000083 |
| 37.26 | 1 | 0.000083 |
| 64.87 | 1 | 0.000083 |
| 20.04 | 1 | 0.000083 |
| 300.52 | 1 | 0.000083 |
| 29.99 | 1 | 0.000083 |
| 39.06 | 1 | 0.000083 |
| 31.50 | 1 | 0.000083 |
| 246.42 | 1 | 0.000083 |
| 9.62 | 1 | 0.000083 |
| 20.50 | 1 | 0.000083 |
| 633.00 | 1 | 0.000083 |
| 510.00 | 1 | 0.000083 |
| 796.00 | 1 | 0.000083 |
| 436.00 | 1 | 0.000083 |
| 2013.00 | 1 | 0.000083 |
| 10.03 | 1 | 0.000083 |
| 36.07 | 1 | 0.000083 |
| 601.00 | 1 | 0.000083 |
| 183.31 | 1 | 0.000083 |
| 12.52 | 1 | 0.000083 |
| 4000.00 | 1 | 0.000083 |
| 242.50 | 1 | 0.000083 |
| 83.50 | 1 | 0.000083 |
| 33.83 | 1 | 0.000083 |
| 605.00 | 1 | 0.000083 |
| 8.25 | 1 | 0.000083 |
| 24.02 | 1 | 0.000083 |
| 24.64 | 1 | 0.000083 |
| 5.05 | 1 | 0.000083 |
| 13.25 | 1 | 0.000083 |
| 25.07 | 1 | 0.000083 |
| 125.34 | 1 | 0.000083 |
| 18.09 | 1 | 0.000083 |
| 15.25 | 1 | 0.000083 |
| 12.60 | 1 | 0.000083 |
| 277.00 | 1 | 0.000083 |
| 690.00 | 1 | 0.000083 |
| 362.00 | 1 | 0.000083 |
| 138.15 | 1 | 0.000083 |
| 10000.00 | 1 | 0.000083 |
| 25.60 | 1 | 0.000083 |
| 16.20 | 1 | 0.000083 |
| 11.21 | 1 | 0.000083 |
| 50.40 | 1 | 0.000083 |
| 2400.00 | 1 | 0.000083 |
| 666.00 | 1 | 0.000083 |
| 241.00 | 1 | 0.000083 |
| 171.00 | 1 | 0.000083 |
| 280.36 | 1 | 0.000083 |
| 9.03 | 1 | 0.000083 |
| 5.60 | 1 | 0.000083 |
| 2253.80 | 1 | 0.000083 |
| 97.50 | 1 | 0.000083 |
| 24.40 | 1 | 0.000083 |
| 12.05 | 1 | 0.000083 |
| 258.44 | 1 | 0.000083 |
| 7.58 | 1 | 0.000083 |
| 7512.65 | 1 | 0.000083 |
| 5.87 | 1 | 0.000083 |
| 8.56 | 1 | 0.000083 |
| 458.00 | 1 | 0.000083 |
| 32.41 | 1 | 0.000083 |
| 3005.00 | 1 | 0.000083 |
| 1715.00 | 1 | 0.000083 |
| 209.00 | 1 | 0.000083 |
| 59.02 | 1 | 0.000083 |
| 366.00 | 1 | 0.000083 |
| 38.85 | 1 | 0.000083 |
| 867.00 | 1 | 0.000083 |
| 42.94 | 1 | 0.000083 |
| 9.99 | 1 | 0.000083 |
| 43.30 | 1 | 0.000083 |
| 2038.00 | 1 | 0.000083 |
| 2.80 | 1 | 0.000083 |
| 2095.00 | 1 | 0.000083 |
| 27.80 | 1 | 0.000083 |
| 12000.00 | 1 | 0.000083 |
| 710.00 | 1 | 0.000083 |
| 1803.00 | 1 | 0.000083 |
| 8.35 | 1 | 0.000083 |
| 89.25 | 1 | 0.000083 |
| 18.05 | 1 | 0.000083 |
| 1800.00 | 1 | 0.000083 |
| 590.00 | 1 | 0.000083 |
| 336.00 | 1 | 0.000083 |
| 225.35 | 1 | 0.000083 |
| 65.86 | 1 | 0.000083 |
| 550.75 | 1 | 0.000083 |
| 30.01 | 1 | 0.000083 |
| 59.50 | 1 | 0.000083 |
| 90.10 | 1 | 0.000083 |
| 228.00 | 1 | 0.000083 |
| 206.00 | 1 | 0.000083 |
| 64.90 | 1 | 0.000083 |
| 77.50 | 1 | 0.000083 |
| 176.00 | 1 | 0.000083 |
| 680.50 | 1 | 0.000083 |
| 150.15 | 1 | 0.000083 |
| 840.00 | 1 | 0.000083 |
| 42.50 | 1 | 0.000083 |
| 15.78 | 1 | 0.000083 |
| 6.35 | 1 | 0.000083 |
| 72.30 | 1 | 0.000083 |
| 288.49 | 1 | 0.000083 |
| 1060.00 | 1 | 0.000083 |
| 30.06 | 1 | 0.000083 |
| 8.80 | 1 | 0.000083 |
| 0.90 | 1 | 0.000083 |
| 9.08 | 1 | 0.000083 |
| 330.56 | 1 | 0.000083 |
| 13.88 | 1 | 0.000083 |
| 6.67 | 1 | 0.000083 |
| 0.77 | 1 | 0.000083 |
| 30.03 | 1 | 0.000083 |
| 6666.00 | 1 | 0.000083 |
| 23.08 | 1 | 0.000083 |
| 18.30 | 1 | 0.000083 |
| 168.28 | 1 | 0.000083 |
| 95.05 | 1 | 0.000083 |
| 31.40 | 1 | 0.000083 |
| 2700.00 | 1 | 0.000083 |
| 33.30 | 1 | 0.000083 |
| 2200.00 | 1 | 0.000083 |
| 100.97 | 1 | 0.000083 |
| 1275.74 | 1 | 0.000083 |
| 49.54 | 1 | 0.000083 |
| 3.88 | 1 | 0.000083 |
| 190.20 | 1 | 0.000083 |
| 34.33 | 1 | 0.000083 |
| 462.00 | 1 | 0.000083 |
| 751.27 | 1 | 0.000083 |
| 95.56 | 1 | 0.000083 |
| 300.01 | 1 | 0.000083 |
| 3035.00 | 1 | 0.000083 |
| 612.00 | 1 | 0.000083 |
| 1620.00 | 1 | 0.000083 |
| 52.02 | 1 | 0.000083 |
| 16.33 | 1 | 0.000083 |
| 16.16 | 1 | 0.000083 |
| 252.43 | 1 | 0.000083 |
| 11.97 | 1 | 0.000083 |
| 1150.00 | 1 | 0.000083 |
| 6.59 | 1 | 0.000083 |
| 68.43 | 1 | 0.000083 |
| 333.76 | 1 | 0.000083 |
| 1000000.00 | 1 | 0.000083 |
| 5555.00 | 1 | 0.000083 |
| 11.01 | 1 | 0.000083 |
# Vamos a realizar analisis por cada variable
var = "npe03__date_established__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__date_established__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npe03__date_established__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2004-01-01 | 5000 | 0.417290 |
| 2000-02-01 | 4604 | 0.384241 |
| 1994-10-01 | 3829 | 0.319561 |
| 2000-01-01 | 3810 | 0.317975 |
| 1995-02-01 | 3377 | 0.281838 |
| ... | ... | ... |
| 2000-09-08 | 1 | 0.000083 |
| 1991-02-16 | 1 | 0.000083 |
| 1993-11-08 | 1 | 0.000083 |
| 1992-09-30 | 1 | 0.000083 |
| 2003-05-06 | 1 | 0.000083 |
7944 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npe03__installment_period__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__installment_period__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npe03__installment_period__c es 28832. Lo que supone un 2.4062620231729577%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Monthly | 928070 | 77.454897 |
| Yearly | 125456 | 10.470311 |
| Quarterly | 89401 | 7.461232 |
| 28832 | 2.406262 | |
| Semestral | 20047 | 1.673083 |
| Bimensual | 6401 | 0.534215 |
# Vamos a realizar analisis por cada variable
var = "npe03__open_ended_status__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__open_ended_status__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npe03__open_ended_status__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Closed | 715235 | 59.692107 |
| Open | 482947 | 40.305807 |
| None | 24 | 0.002003 |
| Close | 1 | 0.000083 |
# Vamos a realizar analisis por cada variable
var = "npe03__paid_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__paid_amount__c es 1899. Lo que supone un 0.15848680570218668% El nº de vacios para la variable npe03__paid_amount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.00 | 465514 | 38.912554 |
| 670.00 | 40774 | 3.408320 |
| 1005.00 | 22332 | 1.866743 |
| 1340.00 | 14305 | 1.195762 |
| 335.00 | 11733 | 0.980767 |
| ... | ... | ... |
| 659.53 | 1 | 0.000084 |
| 220.04 | 1 | 0.000084 |
| 277.32 | 1 | 0.000084 |
| 384.24 | 1 | 0.000084 |
| 970.45 | 1 | 0.000084 |
7095 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npe03__recurring_donation_campaign__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__recurring_donation_campaign__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npe03__recurring_donation_campaign__c es 1. Lo que supone un 8.3458033545122e-05%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mqtnQAA | 55336 | 4.618234 |
| 7013Y000001mr4CQAQ | 38368 | 3.202118 |
| 7013Y000001mrCzQAI | 35953 | 3.000567 |
| 7013Y000001mr2DQAQ | 32217 | 2.688767 |
| 7013Y000001mr2cQAA | 27436 | 2.289755 |
| ... | ... | ... |
| 7013Y000001mrZJQAY | 1 | 0.000083 |
| 7013Y000001mrgaQAA | 1 | 0.000083 |
| 7013Y000001mrHlQAI | 1 | 0.000083 |
| 7013Y000001vCNjQAM | 1 | 0.000083 |
| 7013Y000001mrKFQAY | 1 | 0.000083 |
2746 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npe03__total_paid_installments__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npe03__total_paid_installments__c es 1899. Lo que supone un 0.15848680570218668% El nº de vacios para la variable npe03__total_paid_installments__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 465514 | 38.912554 |
| 67.0 | 240168 | 20.075766 |
| 6.0 | 45376 | 3.793003 |
| 1.0 | 34577 | 2.890309 |
| 5.0 | 32738 | 2.736586 |
| 2.0 | 26558 | 2.219997 |
| 22.0 | 22576 | 1.887139 |
| 3.0 | 20987 | 1.754314 |
| 4.0 | 18250 | 1.525527 |
| 23.0 | 17528 | 1.465175 |
| 11.0 | 11887 | 0.993640 |
| 7.0 | 10423 | 0.871264 |
| 8.0 | 10076 | 0.842258 |
| 12.0 | 9527 | 0.796367 |
| 9.0 | 9273 | 0.775135 |
| 10.0 | 8725 | 0.729327 |
| 13.0 | 8149 | 0.681179 |
| 16.0 | 6981 | 0.583545 |
| 14.0 | 6895 | 0.576357 |
| 20.0 | 6394 | 0.534478 |
| 19.0 | 6183 | 0.516840 |
| 15.0 | 6163 | 0.515168 |
| 21.0 | 6139 | 0.513162 |
| 17.0 | 5729 | 0.478890 |
| 18.0 | 5632 | 0.470782 |
| 33.0 | 5537 | 0.462841 |
| 25.0 | 4887 | 0.408507 |
| 24.0 | 4797 | 0.400984 |
| 27.0 | 4743 | 0.396470 |
| 34.0 | 4673 | 0.390618 |
| 26.0 | 4623 | 0.386439 |
| 66.0 | 4569 | 0.381925 |
| 28.0 | 4163 | 0.347987 |
| 31.0 | 4105 | 0.343139 |
| 29.0 | 4099 | 0.342638 |
| 36.0 | 4006 | 0.334864 |
| 30.0 | 3949 | 0.330099 |
| 42.0 | 3881 | 0.324415 |
| 32.0 | 3729 | 0.311709 |
| 45.0 | 3719 | 0.310873 |
| 37.0 | 3706 | 0.309786 |
| 43.0 | 3573 | 0.298669 |
| 41.0 | 3531 | 0.295158 |
| 39.0 | 3505 | 0.292985 |
| 44.0 | 3414 | 0.285378 |
| 65.0 | 3406 | 0.284709 |
| 38.0 | 3376 | 0.282202 |
| 48.0 | 3373 | 0.281951 |
| 57.0 | 3286 | 0.274678 |
| 60.0 | 3282 | 0.274344 |
| 61.0 | 3236 | 0.270499 |
| 40.0 | 3194 | 0.266988 |
| 46.0 | 3189 | 0.266570 |
| 62.0 | 3154 | 0.263644 |
| 49.0 | 3144 | 0.262809 |
| 56.0 | 3097 | 0.258880 |
| 55.0 | 3076 | 0.257124 |
| 35.0 | 3033 | 0.253530 |
| 63.0 | 2998 | 0.250604 |
| 50.0 | 2997 | 0.250521 |
| 51.0 | 2994 | 0.250270 |
| 54.0 | 2966 | 0.247929 |
| 58.0 | 2847 | 0.237982 |
| 64.0 | 2830 | 0.236561 |
| 52.0 | 2820 | 0.235725 |
| 53.0 | 2740 | 0.229038 |
| 59.0 | 2635 | 0.220261 |
| 47.0 | 2622 | 0.219174 |
| 68.0 | 296 | 0.024743 |
| 69.0 | 29 | 0.002424 |
| 72.0 | 5 | 0.000418 |
| 70.0 | 3 | 0.000251 |
| 73.0 | 3 | 0.000251 |
| 75.0 | 3 | 0.000251 |
| 84.0 | 3 | 0.000251 |
| 78.0 | 2 | 0.000167 |
| 110.0 | 1 | 0.000084 |
| 159.0 | 1 | 0.000084 |
| 82.0 | 1 | 0.000084 |
| 74.0 | 1 | 0.000084 |
| 126.0 | 1 | 0.000084 |
| 141.0 | 1 | 0.000084 |
| 124.0 | 1 | 0.000084 |
| 88.0 | 1 | 0.000084 |
| 90.0 | 1 | 0.000084 |
| 123.0 | 1 | 0.000084 |
| 121.0 | 1 | 0.000084 |
| 93.0 | 1 | 0.000084 |
# Vamos a realizar analisis por cada variable
var = "npsp4hub__payment_method__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_re_donation)
El nº de nulos para la variable npsp4hub__payment_method__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npsp4hub__payment_method__c es 19192. Lo que supone un 1.6017265797979814%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Direct Debit | 1172815 | 97.880834 |
| 19192 | 1.601727 | |
| CreditCard | 6124 | 0.511097 |
| ACMA | 76 | 0.006343 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_re_donation.append(var)
col_to_delete_re_donation
['isdeleted', 'msf_cancelationdate__c', 'msf_cancelationreason__c', 'npsp4hub__payment_method__c']
# Vamos a analizar la tabla recurring donation
df = df_mod_cuota
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_mod_cuota=list()
# Vamos a realizar analisis por cada variable
var = "name"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable name es 0. Lo que supone un 0.0% El nº de vacios para la variable name es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| I - 1606380943266 | 4 | 0.00020 |
| I - 1607861743589 | 4 | 0.00020 |
| I - 1607861743594 | 4 | 0.00020 |
| I - 1604071257275 | 4 | 0.00020 |
| I - 1606811457416 | 4 | 0.00020 |
| ... | ... | ... |
| I - 1582731654437693325 | 1 | 0.00005 |
| A - 1582731654437693317 | 1 | 0.00005 |
| A - 1582731654437693309 | 1 | 0.00005 |
| A - 1582731654437693301 | 1 | 0.00005 |
| D - 1688925499163 | 1 | 0.00005 |
1994102 rows × 2 columns
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_mod_cuota.append(var)
col_to_delete_mod_cuota
['name']
# Vamos a realizar analisis por cada variable
var = "msf_changeamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changeamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_changeamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.000000e+01 | 407031 | 20.320876 |
| 5.000000e+00 | 294167 | 14.686181 |
| 1.500000e+01 | 152100 | 7.593538 |
| 2.000000e+00 | 133031 | 6.641525 |
| 3.000000e+00 | 121177 | 6.049718 |
| ... | ... | ... |
| 1.237500e+03 | 1 | 0.000050 |
| 2.480000e+03 | 1 | 0.000050 |
| 1.343000e+02 | 1 | 0.000050 |
| 1.156700e+02 | 1 | 0.000050 |
| 6.507972e+08 | 1 | 0.000050 |
2597 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_changeannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changeannualizedquota__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_changeannualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 385981 | 19.269962 |
| 6.000000e+01 | 301818 | 15.068155 |
| 2.400000e+01 | 133909 | 6.685358 |
| 1.800000e+02 | 132844 | 6.632189 |
| 3.600000e+01 | 119288 | 5.955410 |
| ... | ... | ... |
| 1.389000e+01 | 1 | 0.000050 |
| 6.490920e+03 | 1 | 0.000050 |
| 2.939900e+02 | 1 | 0.000050 |
| 2.170800e+02 | 1 | 0.000050 |
| 7.809566e+09 | 1 | 0.000050 |
3658 rows × 2 columns
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_mod_cuota.append("msf_changeamount__c")
col_to_delete_mod_cuota
['name', 'msf_changeamount__c']
# Vamos a realizar analisis por cada variable
var = "msf_changetype__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changetype__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_changetype__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Initial | 1174130 | 58.618016 |
| Increase | 767916 | 38.337929 |
| Decrease | 60927 | 3.041758 |
| Changes_without_variation_annualized_fee | 46 | 0.002297 |
# Vamos a realizar analisis por cada variable
var = "msf_leadsource1__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_leadsource1__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_leadsource1__c es 324788. Lo que supone un 16.21492357286676%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Telemarketing | 832085 | 41.541543 |
| Persona a persona | 371059 | 18.524987 |
| 324788 | 16.214924 | |
| Web MSF | 105789 | 5.281478 |
| Cupón | 86079 | 4.297463 |
| Teléfono campaña | 80095 | 3.998714 |
| Personal con tablet | 74862 | 3.737458 |
| Entidad financiera | 67438 | 3.366818 |
| Teléfono SAS | 17534 | 0.875379 |
| Teléfono web | 12659 | 0.631996 |
| Web terceros | 12565 | 0.627303 |
| Email a SAS | 10538 | 0.526106 |
| Web campaña | 4192 | 0.209284 |
| Web MSF Mi perfil | 939 | 0.046879 |
| Cloud page | 922 | 0.046031 |
| Otro | 542 | 0.027059 |
| Teléfono Officers | 441 | 0.022017 |
| Correo postal sin cupón | 227 | 0.011333 |
| Email a Empresas | 207 | 0.010334 |
| Email a officers Mid Donors | 23 | 0.001148 |
| Email a One to one | 10 | 0.000499 |
| Email a Bodas | 8 | 0.000399 |
| Teléfono Herencias y Legados | 6 | 0.000300 |
| Email a Iniciativas Solidarias | 4 | 0.000200 |
| n/a | 3 | 0.000150 |
| SMS | 2 | 0.000100 |
| Email Director/a General | 1 | 0.000050 |
| Redes Sociales | 1 | 0.000050 |
# Vamos a realizar analisis por cada variable
var = "msf_leadsource2__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_leadsource2__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_leadsource2__c es 324964. Lo que supone un 16.22371030928813%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Telemarketing | 832029 | 41.538747 |
| Persona a persona | 445916 | 22.262195 |
| 324964 | 16.223710 | |
| Formulario web | 124405 | 6.210875 |
| Cupón | 86069 | 4.296964 |
| Teléfono campaña | 80020 | 3.994970 |
| Entidad financiera | 67431 | 3.366468 |
| Teléfonos SAS | 17534 | 0.875379 |
| Teléfono web | 12654 | 0.631746 |
| 10779 | 0.538138 | |
| Otro | 541 | 0.027009 |
| Teléfonos Officers | 441 | 0.022017 |
| Correo postal sin cupón | 227 | 0.011333 |
| Teléfono Herencias y Legados | 6 | 0.000300 |
| SMS | 2 | 0.000100 |
| Redes Sociales | 1 | 0.000050 |
# Vamos a realizar analisis por cada variable
var = "msf_leadsource3__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_leadsource3__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_leadsource3__c es 324964. Lo que supone un 16.22371030928813%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Teléfono | 942686 | 47.063258 |
| Personal | 445916 | 22.262195 |
| 324964 | 16.223710 | |
| Online | 135185 | 6.749062 |
| Correo postal | 86296 | 4.308297 |
| Entidad financiera | 67431 | 3.366468 |
| Otro | 541 | 0.027009 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_mod_cuota.append("msf_leadsource1__c")
col_to_delete_mod_cuota.append("msf_leadsource2__c")
col_to_delete_mod_cuota
['name', 'msf_changeamount__c', 'msf_leadsource1__c', 'msf_leadsource2__c']
# Vamos a realizar analisis por cada variable
var = "msf_newamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_newamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_newamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.000000e+01 | 397732 | 19.856626 |
| 1.500000e+01 | 214087 | 10.688216 |
| 2.000000e+01 | 156745 | 7.825438 |
| 5.000000e+00 | 152635 | 7.620247 |
| 1.200000e+01 | 108041 | 5.393908 |
| ... | ... | ... |
| 3.030000e+02 | 1 | 0.000050 |
| 1.809000e+01 | 1 | 0.000050 |
| 4.360000e+02 | 1 | 0.000050 |
| 6.851500e+02 | 1 | 0.000050 |
| 6.507972e+08 | 1 | 0.000050 |
1284 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_newannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_newannualizedquota__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_newannualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 411248 | 20.531408 |
| 1.800000e+02 | 201969 | 10.083229 |
| 6.000000e+01 | 183545 | 9.163418 |
| 2.400000e+02 | 139200 | 6.949510 |
| 1.440000e+02 | 102569 | 5.120720 |
| ... | ... | ... |
| 1.262040e+03 | 1 | 0.000050 |
| 6.970000e+01 | 1 | 0.000050 |
| 6.851500e+02 | 1 | 0.000050 |
| 2.404048e+05 | 1 | 0.000050 |
| 7.809566e+09 | 1 | 0.000050 |
1681 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_newrecurringperiod__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_newrecurringperiod__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_newrecurringperiod__c es 235. Lo que supone un 0.01173229010808185%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Monthly | 1536735 | 76.720940 |
| Yearly | 251355 | 12.548808 |
| Quarterly | 170741 | 8.524183 |
| Semestral | 34468 | 1.720802 |
| Bimensual | 9485 | 0.473535 |
| 235 | 0.011732 |
# Vamos a realizar analisis por cada variable
var = "msf_changedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_mod_cuota)
El nº de nulos para la variable msf_changedate__c es 186. Lo que supone un 0.00928598280894989% El nº de vacios para la variable msf_changedate__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2018-01-03 | 15448 | 0.771307 |
| 2017-02-02 | 13291 | 0.663610 |
| 2017-12-04 | 12659 | 0.632055 |
| 2018-02-01 | 11043 | 0.551369 |
| 2014-12-02 | 10917 | 0.545078 |
| ... | ... | ... |
| 1990-01-10 | 1 | 0.000050 |
| 2000-12-07 | 1 | 0.000050 |
| 1991-09-03 | 1 | 0.000050 |
| 1996-11-09 | 1 | 0.000050 |
| 1991-11-18 | 1 | 0.000050 |
7992 rows × 2 columns
# Vamos a analizar la tabla contactos
df=df_contactos
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_contactos=list()
# Vamos a realizar analisis por cada variable
var = "msf_seniority__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_seniority__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_seniority__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 534123 | 29.617244 |
| 1.0 | 87701 | 4.863041 |
| 6.0 | 84533 | 4.687374 |
| 8.0 | 76878 | 4.262903 |
| 7.0 | 74742 | 4.144461 |
| 9.0 | 73643 | 4.083521 |
| 13.0 | 61510 | 3.410744 |
| 2.0 | 55231 | 3.062572 |
| 5.0 | 53150 | 2.947180 |
| 12.0 | 52314 | 2.900823 |
| 10.0 | 50959 | 2.825688 |
| 4.0 | 49842 | 2.763750 |
| 29.0 | 48213 | 2.673422 |
| 18.0 | 44670 | 2.476962 |
| 3.0 | 42963 | 2.382308 |
| 11.0 | 41647 | 2.309336 |
| 14.0 | 41012 | 2.274125 |
| 19.0 | 40508 | 2.246178 |
| 17.0 | 36854 | 2.043563 |
| 15.0 | 33506 | 1.857915 |
| 16.0 | 32733 | 1.815052 |
| 20.0 | 27078 | 1.501481 |
| 23.0 | 26493 | 1.469043 |
| 22.0 | 21703 | 1.203436 |
| 25.0 | 19070 | 1.057436 |
| 24.0 | 17734 | 0.983354 |
| 21.0 | 13985 | 0.775471 |
| 28.0 | 13981 | 0.775250 |
| 27.0 | 13960 | 0.774085 |
| 31.0 | 13612 | 0.754789 |
| 26.0 | 8493 | 0.470939 |
| 30.0 | 7239 | 0.401404 |
| 32.0 | 1634 | 0.090606 |
| 34.0 | 552 | 0.030609 |
| 35.0 | 550 | 0.030498 |
| 33.0 | 370 | 0.020517 |
| 36.0 | 201 | 0.011145 |
| 37.0 | 32 | 0.001774 |
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year__c"
# Analizamos nulos
count_nulos(df_contactos,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__best_gift_year__c es 709207. Lo que supone un 39.32569192184401%
['npo02__best_gift_year__c']
# Analizamos posibles valores de la variable
freq_variables(df_contactos,var)
| # Tot | % Tot | |
|---|---|---|
| 709207 | 39.325692 | |
| 2018 | 303667 | 16.838405 |
| 2022 | 185032 | 10.260067 |
| 2021 | 93074 | 5.160975 |
| 2020 | 90828 | 5.036434 |
| 2019 | 77054 | 4.272662 |
| 2023 | 55899 | 3.099612 |
| 2010 | 29210 | 1.619701 |
| 1994 | 28224 | 1.565027 |
| 2017 | 21245 | 1.178040 |
| 2005 | 15932 | 0.883433 |
| 2014 | 14681 | 0.814065 |
| 2011 | 14643 | 0.811958 |
| 2004 | 13160 | 0.729725 |
| 2000 | 12659 | 0.701944 |
| 2015 | 11996 | 0.665181 |
| 2001 | 11403 | 0.632299 |
| 1998 | 11363 | 0.630081 |
| 2013 | 10940 | 0.606626 |
| 2016 | 9948 | 0.551619 |
| 2003 | 9537 | 0.528829 |
| 2008 | 8465 | 0.469386 |
| 1999 | 8142 | 0.451476 |
| 2009 | 7599 | 0.421366 |
| 1996 | 6869 | 0.380888 |
| 2012 | 6795 | 0.376784 |
| 2006 | 6723 | 0.372792 |
| 1992 | 6238 | 0.345899 |
| 2007 | 5562 | 0.308414 |
| 2002 | 4753 | 0.263555 |
| 1997 | 4491 | 0.249027 |
| 1995 | 4064 | 0.225350 |
| 1993 | 2470 | 0.136962 |
| 1991 | 624 | 0.034601 |
| 1989 | 435 | 0.024121 |
| 1990 | 212 | 0.011755 |
| 1988 | 187 | 0.010369 |
| 1987 | 88 | 0.004880 |
# Vamos a realizar analisis por cada variable
var = "msf_birthyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_birthyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_birthyear__c es 978577. Lo que supone un 54.26232062543425%
['npo02__best_gift_year__c', 'msf_birthyear__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 978577 | 54.262321 | |
| 1964 | 18538 | 1.027936 |
| 1963 | 18278 | 1.013519 |
| 1965 | 18234 | 1.011080 |
| 1968 | 18067 | 1.001819 |
| 1959 | 17962 | 0.995997 |
| 1958 | 17951 | 0.995387 |
| 1962 | 17871 | 0.990951 |
| 1966 | 17855 | 0.990064 |
| 1973 | 17816 | 0.987901 |
| 1974 | 17776 | 0.985683 |
| 1975 | 17754 | 0.984463 |
| 1957 | 17705 | 0.981746 |
| 1961 | 17701 | 0.981525 |
| 1960 | 17684 | 0.980582 |
| 1972 | 17602 | 0.976035 |
| 1967 | 17569 | 0.974205 |
| 1976 | 17476 | 0.969048 |
| 1971 | 17399 | 0.964779 |
| 1969 | 17397 | 0.964668 |
| 1970 | 17285 | 0.958457 |
| 1977 | 16927 | 0.938606 |
| 1978 | 16689 | 0.925409 |
| 1956 | 16033 | 0.889034 |
| 1979 | 16023 | 0.888479 |
| 1980 | 15320 | 0.849498 |
| 1955 | 14913 | 0.826929 |
| 1981 | 14485 | 0.803197 |
| 1954 | 13777 | 0.763938 |
| 1982 | 13359 | 0.740760 |
| 1953 | 13105 | 0.726675 |
| 1983 | 12665 | 0.702277 |
| 1952 | 12655 | 0.701723 |
| 1984 | 11754 | 0.651762 |
| 1951 | 11508 | 0.638121 |
| 1950 | 11123 | 0.616773 |
| 1985 | 10843 | 0.601247 |
| 1949 | 10698 | 0.593207 |
| 1948 | 10334 | 0.573023 |
| 1986 | 9804 | 0.543634 |
| 1987 | 9147 | 0.507203 |
| 1947 | 9075 | 0.503211 |
| 1988 | 8543 | 0.473711 |
| 1989 | 8272 | 0.458684 |
| 1946 | 8261 | 0.458074 |
| 1945 | 8234 | 0.456577 |
| 1991 | 7771 | 0.430904 |
| 1990 | 7748 | 0.429628 |
| 1992 | 7676 | 0.425636 |
| 1993 | 7401 | 0.410387 |
| 1994 | 7375 | 0.408945 |
| 1996 | 7255 | 0.402291 |
| 1995 | 7246 | 0.401792 |
| 1944 | 7141 | 0.395970 |
| 1943 | 7129 | 0.395305 |
| 1997 | 7041 | 0.390425 |
| 1999 | 6720 | 0.372626 |
| 1998 | 6627 | 0.367469 |
| 2000 | 6424 | 0.356212 |
| 2001 | 5559 | 0.308248 |
| 1942 | 5491 | 0.304477 |
| 1940 | 5175 | 0.286955 |
| 1941 | 4961 | 0.275089 |
| 2002 | 4619 | 0.256125 |
| 2003 | 3607 | 0.200009 |
| 1936 | 3586 | 0.198845 |
| 1938 | 3348 | 0.185647 |
| 1937 | 3315 | 0.183818 |
| 1939 | 3294 | 0.182653 |
| 1935 | 3234 | 0.179326 |
| 1934 | 2862 | 0.158699 |
| 1933 | 2513 | 0.139346 |
| 1932 | 2359 | 0.130807 |
| 1930 | 2069 | 0.114727 |
| 2004 | 2032 | 0.112675 |
| 1931 | 2025 | 0.112287 |
| 1929 | 1485 | 0.082344 |
| 1928 | 1386 | 0.076854 |
| 1927 | 1123 | 0.062271 |
| 1926 | 959 | 0.053177 |
| 1925 | 860 | 0.047687 |
| 1924 | 743 | 0.041200 |
| 1923 | 589 | 0.032660 |
| 1922 | 545 | 0.030220 |
| 1921 | 419 | 0.023234 |
| 1920 | 338 | 0.018742 |
| 2020 | 313 | 0.017356 |
| 1919 | 295 | 0.016358 |
| 2005 | 238 | 0.013197 |
| 1918 | 194 | 0.010757 |
| 2019 | 181 | 0.010036 |
| 2006 | 163 | 0.009038 |
| 1917 | 158 | 0.008761 |
| 1916 | 132 | 0.007319 |
| 2017 | 131 | 0.007264 |
| 2008 | 123 | 0.006820 |
| 2007 | 116 | 0.006432 |
| 2016 | 114 | 0.006321 |
| 1915 | 103 | 0.005711 |
| 2014 | 93 | 0.005157 |
| 2021 | 85 | 0.004713 |
| 2015 | 85 | 0.004713 |
| 2013 | 85 | 0.004713 |
| 2018 | 77 | 0.004270 |
| 1914 | 75 | 0.004159 |
| 2012 | 74 | 0.004103 |
| 2010 | 71 | 0.003937 |
| 2009 | 68 | 0.003771 |
| 2011 | 58 | 0.003216 |
| 1913 | 57 | 0.003161 |
| 1911 | 41 | 0.002273 |
| 1912 | 33 | 0.001830 |
| 2022 | 23 | 0.001275 |
| 1909 | 22 | 0.001220 |
| 1910 | 21 | 0.001164 |
| 2023 | 19 | 0.001054 |
| 1906 | 12 | 0.000665 |
| 1904 | 12 | 0.000665 |
| 1908 | 11 | 0.000610 |
| 1907 | 9 | 0.000499 |
| 1905 | 8 | 0.000444 |
| 1903 | 6 | 0.000333 |
| 1900 | 6 | 0.000333 |
| 1902 | 5 | 0.000277 |
| 1901 | 3 | 0.000166 |
| 1897 | 2 | 0.000111 |
| 1800 | 1 | 0.000055 |
| 1893 | 1 | 0.000055 |
| 1712 | 1 | 0.000055 |
# Vamos a realizar analisis por cada variable
var = "msf_entrycampaign__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrycampaign__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_entrycampaign__c es 8854. Lo que supone un 0.49095634458769705%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mrCzQAI | 184255 | 10.216982 |
| 7013Y000001vYkXQAU | 60467 | 3.352909 |
| 7013Y000001mrC7QAI | 60346 | 3.346200 |
| 7013Y000001mr1MQAQ | 45720 | 2.535185 |
| 7013Y000001mr4CQAQ | 39785 | 2.206087 |
| ... | ... | ... |
| 7013Y000001vQQaQAM | 1 | 0.000055 |
| 7013Y000001mrPEQAY | 1 | 0.000055 |
| 7013Y000001va2hQAA | 1 | 0.000055 |
| 7013Y000001mrHCQAY | 1 | 0.000055 |
| 7013Y000001mre3QAA | 1 | 0.000055 |
5356 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "leadsource"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable leadsource es 0. Lo que supone un 0.0% El nº de vacios para la variable leadsource es 8860. Lo que supone un 0.491289045973232%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Otro | 518507 | 28.751333 |
| Telemarketing | 413225 | 22.913422 |
| Persona a persona | 354960 | 19.682614 |
| Web MSF | 137215 | 7.608603 |
| Web terceros | 96714 | 5.362814 |
| Cupón | 81431 | 4.515368 |
| Personal con tablet | 73165 | 4.057016 |
| Teléfono campaña | 39125 | 2.169490 |
| Web campaña | 28490 | 1.579777 |
| Eventos | 13171 | 0.730335 |
| 8860 | 0.491289 | |
| Redes Sociales | 7426 | 0.411773 |
| Entidad financiera | 6672 | 0.369964 |
| Email a Bodas | 5654 | 0.313516 |
| Teléfono web | 5216 | 0.289228 |
| Plataforma iniciativas | 4622 | 0.256291 |
| Teléfono SAS | 2520 | 0.139735 |
| Email a Empresas | 2286 | 0.126759 |
| Email a SAS | 2206 | 0.122323 |
| Email a Iniciativas Solidarias | 591 | 0.032771 |
| Correo postal sin cupón | 475 | 0.026339 |
| Email a One to one | 364 | 0.020184 |
| Teléfono Officers | 185 | 0.010258 |
| Email herencias | 143 | 0.007929 |
| Teléfono Herencias y Legados | 98 | 0.005434 |
| TelEfono officers | 56 | 0.003105 |
| SMS | 16 | 0.000887 |
| Email a officers Mid Donors | 10 | 0.000555 |
| Cloud page | 10 | 0.000555 |
| Email Presidente/a MSF | 2 | 0.000111 |
| Email Director/a General | 2 | 0.000111 |
| Tel?fono SAS | 2 | 0.000111 |
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaigncolaborationchannel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaigncolaborationchannel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaigncolaborationchannel__c es 643136. Lo que supone un 35.66203971456439%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 643136 | 35.662040 | |
| Persona a persona | 311433 | 17.269032 |
| Otro | 287825 | 15.959963 |
| Telemarketing | 171639 | 9.517422 |
| Web MSF | 104919 | 5.817783 |
| Cupón | 76517 | 4.242885 |
| Personal con tablet | 68111 | 3.776771 |
| Web terceros | 65274 | 3.619458 |
| Teléfono campaña | 37964 | 2.105113 |
| Web campaña | 11214 | 0.621819 |
| Teléfono web | 5200 | 0.288341 |
| Entidad financiera | 4822 | 0.267381 |
| Plataforma iniciativas | 4774 | 0.264719 |
| Teléfono SAS | 2876 | 0.159475 |
| Email a SAS | 1945 | 0.107851 |
| Eventos | 1470 | 0.081512 |
| web campaña | 1119 | 0.062049 |
| Email a Bodas | 888 | 0.049240 |
| Email a Empresas | 877 | 0.048630 |
| Correo postal sin cupón | 629 | 0.034878 |
| cupón | 258 | 0.014306 |
| Teléfono Officers | 195 | 0.010813 |
| Web MSF Mi perfil | 189 | 0.010480 |
| Email herencias | 59 | 0.003272 |
| Teléfono Herencias y Legados | 30 | 0.001664 |
| Email a Iniciativas Solidarias | 22 | 0.001220 |
| Email a One to one | 17 | 0.000943 |
| sms | 7 | 0.000388 |
| Email a officers Mid Donors | 4 | 0.000222 |
| Cloud page | 3 | 0.000166 |
| Email a one to one | 2 | 0.000111 |
| Email Director/a General | 1 | 0.000055 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_firstcampaigncolaborationchannel__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c']
# Vamos a realizar analisis por cada variable
var = "npo02__averageamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__averageamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1803419 | 100.0 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("npo02__averageamount__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c']
# Vamos a realizar analisis por cada variable
var = "msf_isactivedonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isactivedonor__c es 26966. Lo que supone un 1.4952709270557756%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Nunca | 1150813 | 63.812847 |
| Exdonante | 512353 | 28.410092 |
| Donante | 113287 | 6.281790 |
| 26966 | 1.495271 |
# Vamos a realizar analisis por cada variable
var = "msf_isactiverecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isactiverecurringdonor__c es 26966. Lo que supone un 1.4952709270557756%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Nunca | 783149 | 43.425793 |
| Baja | 511080 | 28.339504 |
| Socio | 482224 | 26.739432 |
| 26966 | 1.495271 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactiverecurringdonor__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c']
# Vamos a realizar analisis por cada variable
var = "npsp__deceased__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__deceased__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1778101 | 98.596111 |
| True | 25318 | 1.403889 |
# Vamos a realizar analisis por cada variable
var = "msf_begindatemsf__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_begindatemsf__c es 1. Lo que supone un 5.545023092248668e-05% El nº de vacios para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2004-11-10 | 33850 | 1.876991 |
| 2013-03-28 | 19100 | 1.059100 |
| 2015-12-22 | 14283 | 0.791996 |
| 2022-01-14 | 13308 | 0.737932 |
| 2010-03-29 | 11776 | 0.652982 |
| ... | ... | ... |
| 1990-08-31 | 1 | 0.000055 |
| 1991-02-21 | 1 | 0.000055 |
| 1990-08-15 | 1 | 0.000055 |
| 1990-07-19 | 1 | 0.000055 |
| 1994-09-18 | 1 | 0.000055 |
11146 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_fechacambiolevelrelacion__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_fechacambiolevelrelacion__c es 2204. Lo que supone un 0.12221230895316064% El nº de vacios para la variable msf_fechacambiolevelrelacion__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-03-28 | 1471390 | 81.688749 |
| 2022-02-10 | 19708 | 1.094150 |
| 2020-07-20 | 19554 | 1.085601 |
| 2022-01-15 | 13175 | 0.731451 |
| 2021-07-30 | 9205 | 0.511044 |
| 2022-05-11 | 7309 | 0.405782 |
| 2022-03-22 | 7297 | 0.405115 |
| 2020-09-19 | 7118 | 0.395178 |
| 2021-04-15 | 7054 | 0.391625 |
| 2020-09-22 | 6608 | 0.366863 |
| 2022-06-04 | 5376 | 0.298465 |
| 2023-01-26 | 4782 | 0.265487 |
| 2022-06-17 | 4682 | 0.259936 |
| 2022-05-06 | 4607 | 0.255772 |
| 2022-12-03 | 4578 | 0.254162 |
| 2023-02-21 | 4232 | 0.234953 |
| 2022-10-21 | 4112 | 0.228290 |
| 2022-01-02 | 3213 | 0.178380 |
| 2022-09-28 | 2308 | 0.128136 |
| 2020-09-20 | 2281 | 0.126637 |
| 2022-12-20 | 2235 | 0.124083 |
| 2023-01-03 | 1940 | 0.107705 |
| 2020-09-21 | 1746 | 0.096935 |
| 2022-11-17 | 1495 | 0.083000 |
| 2022-06-14 | 1455 | 0.080779 |
| 2021-01-04 | 1360 | 0.075505 |
| 2022-07-22 | 1227 | 0.068121 |
| 2023-01-02 | 1192 | 0.066178 |
| 2023-02-10 | 1026 | 0.056962 |
| 2022-03-23 | 1018 | 0.056517 |
| 2022-03-05 | 1005 | 0.055796 |
| 2023-02-09 | 985 | 0.054685 |
| 2021-05-20 | 974 | 0.054075 |
| 2023-02-23 | 898 | 0.049855 |
| 2022-09-08 | 879 | 0.048800 |
| 2021-01-20 | 821 | 0.045580 |
| 2023-02-15 | 804 | 0.044637 |
| 2023-03-28 | 797 | 0.044248 |
| 2022-12-28 | 784 | 0.043526 |
| 2022-03-12 | 754 | 0.041861 |
| 2022-01-19 | 747 | 0.041472 |
| 2022-10-27 | 708 | 0.039307 |
| 2023-02-11 | 707 | 0.039251 |
| 2023-02-08 | 696 | 0.038641 |
| 2023-04-05 | 682 | 0.037863 |
| 2022-05-10 | 681 | 0.037808 |
| 2022-05-19 | 662 | 0.036753 |
| 2022-03-11 | 660 | 0.036642 |
| 2021-10-07 | 654 | 0.036309 |
| 2022-03-09 | 649 | 0.036031 |
| 2022-12-23 | 646 | 0.035865 |
| 2020-12-04 | 642 | 0.035643 |
| 2022-03-07 | 631 | 0.035032 |
| 2022-03-10 | 623 | 0.034588 |
| 2022-03-15 | 594 | 0.032978 |
| 2022-03-04 | 593 | 0.032922 |
| 2021-06-18 | 593 | 0.032922 |
| 2023-05-13 | 588 | 0.032645 |
| 2023-04-19 | 578 | 0.032089 |
| 2022-09-23 | 577 | 0.032034 |
| 2021-11-18 | 568 | 0.031534 |
| 2022-10-06 | 566 | 0.031423 |
| 2021-03-04 | 558 | 0.030979 |
| 2021-07-28 | 556 | 0.030868 |
| 2022-03-17 | 536 | 0.029758 |
| 2023-02-12 | 533 | 0.029591 |
| 2022-03-08 | 533 | 0.029591 |
| 2022-03-24 | 528 | 0.029314 |
| 2020-09-23 | 520 | 0.028869 |
| 2022-07-07 | 519 | 0.028814 |
| 2023-02-14 | 509 | 0.028259 |
| 2022-03-16 | 508 | 0.028203 |
| 2023-03-16 | 506 | 0.028092 |
| 2023-02-18 | 501 | 0.027815 |
| 2021-01-01 | 496 | 0.027537 |
| 2021-07-08 | 489 | 0.027148 |
| 2023-02-17 | 488 | 0.027093 |
| 2022-03-25 | 488 | 0.027093 |
| 2022-02-24 | 487 | 0.027037 |
| 2021-06-09 | 482 | 0.026760 |
| 2021-02-05 | 480 | 0.026649 |
| 2022-06-08 | 477 | 0.026482 |
| 2022-02-05 | 476 | 0.026427 |
| 2020-10-03 | 475 | 0.026371 |
| 2021-09-07 | 475 | 0.026371 |
| 2023-02-16 | 474 | 0.026316 |
| 2022-11-24 | 470 | 0.026093 |
| 2021-05-13 | 470 | 0.026093 |
| 2021-06-25 | 468 | 0.025982 |
| 2023-03-30 | 465 | 0.025816 |
| 2021-01-06 | 463 | 0.025705 |
| 2022-04-20 | 462 | 0.025649 |
| 2021-08-07 | 461 | 0.025594 |
| 2023-04-26 | 460 | 0.025538 |
| 2022-05-24 | 454 | 0.025205 |
| 2021-01-28 | 454 | 0.025205 |
| 2021-03-11 | 440 | 0.024428 |
| 2021-01-03 | 439 | 0.024372 |
| 2022-02-15 | 435 | 0.024150 |
| 2022-03-18 | 434 | 0.024095 |
| 2020-11-20 | 427 | 0.023706 |
| 2020-11-27 | 426 | 0.023651 |
| 2023-02-07 | 424 | 0.023540 |
| 2022-01-06 | 416 | 0.023096 |
| 2020-11-06 | 413 | 0.022929 |
| 2022-07-01 | 410 | 0.022762 |
| 2021-05-08 | 403 | 0.022374 |
| 2023-04-21 | 400 | 0.022207 |
| 2022-02-03 | 392 | 0.021763 |
| 2021-06-23 | 390 | 0.021652 |
| 2021-12-03 | 388 | 0.021541 |
| 2020-09-25 | 381 | 0.021152 |
| 2022-09-30 | 379 | 0.021041 |
| 2023-05-04 | 376 | 0.020875 |
| 2021-03-18 | 375 | 0.020819 |
| 2023-07-05 | 375 | 0.020819 |
| 2023-03-03 | 374 | 0.020764 |
| 2023-03-01 | 373 | 0.020708 |
| 2023-02-24 | 373 | 0.020708 |
| 2021-11-13 | 373 | 0.020708 |
| 2021-12-22 | 369 | 0.020486 |
| 2021-04-14 | 369 | 0.020486 |
| 2021-04-17 | 367 | 0.020375 |
| 2021-06-03 | 365 | 0.020264 |
| 2020-12-25 | 363 | 0.020153 |
| 2023-01-18 | 363 | 0.020153 |
| 2022-11-30 | 361 | 0.020042 |
| 2023-01-04 | 359 | 0.019931 |
| 2021-05-27 | 357 | 0.019820 |
| 2021-03-06 | 356 | 0.019764 |
| 2023-04-14 | 356 | 0.019764 |
| 2021-04-22 | 355 | 0.019709 |
| 2022-06-23 | 352 | 0.019542 |
| 2021-01-21 | 349 | 0.019376 |
| 2021-06-05 | 349 | 0.019376 |
| 2021-04-29 | 349 | 0.019376 |
| 2023-03-24 | 348 | 0.019320 |
| 2022-05-28 | 348 | 0.019320 |
| 2022-08-06 | 347 | 0.019265 |
| 2022-11-18 | 345 | 0.019154 |
| 2020-12-19 | 343 | 0.019043 |
| 2023-04-20 | 341 | 0.018932 |
| 2021-11-07 | 341 | 0.018932 |
| 2021-02-11 | 338 | 0.018765 |
| 2023-03-10 | 338 | 0.018765 |
| 2022-09-04 | 338 | 0.018765 |
| 2020-12-24 | 338 | 0.018765 |
| 2020-10-24 | 337 | 0.018710 |
| 2020-12-30 | 337 | 0.018710 |
| 2023-01-06 | 336 | 0.018654 |
| 2023-04-12 | 335 | 0.018599 |
| 2022-07-14 | 335 | 0.018599 |
| 2022-04-30 | 332 | 0.018432 |
| 2023-01-19 | 332 | 0.018432 |
| 2022-12-15 | 330 | 0.018321 |
| 2022-09-14 | 330 | 0.018321 |
| 2021-02-18 | 329 | 0.018265 |
| 2023-05-17 | 329 | 0.018265 |
| 2021-09-04 | 324 | 0.017988 |
| 2023-05-31 | 324 | 0.017988 |
| 2021-07-03 | 324 | 0.017988 |
| 2023-02-25 | 324 | 0.017988 |
| 2021-05-29 | 323 | 0.017932 |
| 2023-03-22 | 322 | 0.017877 |
| 2021-12-17 | 322 | 0.017877 |
| 2021-03-25 | 321 | 0.017821 |
| 2022-04-12 | 319 | 0.017710 |
| 2023-04-07 | 319 | 0.017710 |
| 2023-03-07 | 319 | 0.017710 |
| 2023-05-11 | 319 | 0.017710 |
| 2021-12-23 | 318 | 0.017655 |
| 2022-12-10 | 318 | 0.017655 |
| 2023-05-26 | 317 | 0.017599 |
| 2022-07-20 | 316 | 0.017544 |
| 2022-07-21 | 315 | 0.017488 |
| 2023-05-24 | 315 | 0.017488 |
| 2023-03-05 | 315 | 0.017488 |
| 2020-11-17 | 313 | 0.017377 |
| 2023-01-27 | 313 | 0.017377 |
| 2022-12-21 | 312 | 0.017322 |
| 2020-09-24 | 311 | 0.017266 |
| 2022-03-31 | 311 | 0.017266 |
| 2021-02-10 | 310 | 0.017211 |
| 2022-10-14 | 310 | 0.017211 |
| 2022-06-29 | 308 | 0.017100 |
| 2022-12-16 | 307 | 0.017044 |
| 2023-01-14 | 305 | 0.016933 |
| 2022-03-19 | 305 | 0.016933 |
| 2022-05-12 | 304 | 0.016877 |
| 2023-06-15 | 302 | 0.016766 |
| 2022-10-20 | 301 | 0.016711 |
| 2023-01-31 | 301 | 0.016711 |
| 2020-09-29 | 301 | 0.016711 |
| 2022-11-23 | 300 | 0.016655 |
| 2022-11-16 | 299 | 0.016600 |
| 2020-10-29 | 299 | 0.016600 |
| 2023-02-28 | 299 | 0.016600 |
| 2022-11-10 | 298 | 0.016544 |
| 2022-01-20 | 298 | 0.016544 |
| 2023-03-09 | 298 | 0.016544 |
| 2022-07-15 | 298 | 0.016544 |
| 2022-03-01 | 297 | 0.016489 |
| 2023-06-09 | 297 | 0.016489 |
| 2020-10-22 | 297 | 0.016489 |
| 2021-12-28 | 296 | 0.016433 |
| 2023-03-31 | 295 | 0.016378 |
| 2022-09-29 | 295 | 0.016378 |
| 2021-09-09 | 294 | 0.016322 |
| 2022-01-28 | 292 | 0.016211 |
| 2021-12-19 | 292 | 0.016211 |
| 2021-02-25 | 292 | 0.016211 |
| 2022-03-14 | 292 | 0.016211 |
| 2022-05-03 | 291 | 0.016156 |
| 2022-10-12 | 291 | 0.016156 |
| 2021-09-22 | 291 | 0.016156 |
| 2023-04-27 | 291 | 0.016156 |
| 2023-03-23 | 290 | 0.016100 |
| 2022-04-07 | 290 | 0.016100 |
| 2022-06-22 | 290 | 0.016100 |
| 2021-11-30 | 287 | 0.015934 |
| 2023-04-15 | 287 | 0.015934 |
| 2021-01-19 | 287 | 0.015934 |
| 2022-11-11 | 284 | 0.015767 |
| 2021-07-22 | 284 | 0.015767 |
| 2022-12-04 | 284 | 0.015767 |
| 2022-11-26 | 283 | 0.015712 |
| 2023-02-01 | 283 | 0.015712 |
| 2021-06-01 | 281 | 0.015601 |
| 2021-07-29 | 281 | 0.015601 |
| 2023-01-25 | 280 | 0.015545 |
| 2021-04-01 | 279 | 0.015490 |
| 2022-01-08 | 278 | 0.015434 |
| 2023-06-28 | 278 | 0.015434 |
| 2021-06-29 | 278 | 0.015434 |
| 2023-06-21 | 277 | 0.015379 |
| 2022-03-03 | 277 | 0.015379 |
| 2023-06-07 | 276 | 0.015323 |
| 2021-06-10 | 276 | 0.015323 |
| 2021-04-08 | 276 | 0.015323 |
| 2023-03-15 | 275 | 0.015267 |
| 2023-03-17 | 274 | 0.015212 |
| 2022-11-25 | 274 | 0.015212 |
| 2021-11-25 | 273 | 0.015156 |
| 2022-09-15 | 272 | 0.015101 |
| 2023-03-26 | 272 | 0.015101 |
| 2022-08-04 | 272 | 0.015101 |
| 2021-07-15 | 272 | 0.015101 |
| 2022-12-17 | 272 | 0.015101 |
| 2023-06-22 | 271 | 0.015045 |
| 2022-06-10 | 271 | 0.015045 |
| 2023-03-08 | 271 | 0.015045 |
| 2021-07-01 | 271 | 0.015045 |
| 2022-03-29 | 270 | 0.014990 |
| 2022-06-15 | 269 | 0.014934 |
| 2023-07-07 | 269 | 0.014934 |
| 2022-11-09 | 269 | 0.014934 |
| 2022-03-26 | 268 | 0.014879 |
| 2022-05-13 | 268 | 0.014879 |
| 2022-06-09 | 267 | 0.014823 |
| 2020-12-22 | 267 | 0.014823 |
| 2021-12-14 | 265 | 0.014712 |
| 2023-06-16 | 265 | 0.014712 |
| 2023-01-12 | 264 | 0.014657 |
| 2022-10-11 | 264 | 0.014657 |
| 2023-05-12 | 263 | 0.014601 |
| 2021-11-19 | 262 | 0.014546 |
| 2023-06-29 | 261 | 0.014490 |
| 2022-04-09 | 261 | 0.014490 |
| 2022-03-13 | 261 | 0.014490 |
| 2022-04-28 | 261 | 0.014490 |
| 2020-12-11 | 260 | 0.014435 |
| 2022-04-08 | 260 | 0.014435 |
| 2021-10-29 | 260 | 0.014435 |
| 2020-12-17 | 259 | 0.014379 |
| 2022-04-03 | 259 | 0.014379 |
| 2021-02-24 | 258 | 0.014324 |
| 2022-12-14 | 258 | 0.014324 |
| 2021-12-24 | 258 | 0.014324 |
| 2021-10-01 | 258 | 0.014324 |
| 2021-01-14 | 257 | 0.014268 |
| 2021-12-30 | 257 | 0.014268 |
| 2022-10-01 | 256 | 0.014213 |
| 2020-10-06 | 256 | 0.014213 |
| 2021-02-17 | 256 | 0.014213 |
| 2021-12-21 | 256 | 0.014213 |
| 2023-01-10 | 255 | 0.014157 |
| 2021-03-27 | 254 | 0.014102 |
| 2022-11-01 | 252 | 0.013991 |
| 2023-07-08 | 251 | 0.013935 |
| 2022-04-01 | 251 | 0.013935 |
| 2022-10-18 | 251 | 0.013935 |
| 2022-10-15 | 250 | 0.013880 |
| 2022-07-28 | 250 | 0.013880 |
| 2022-04-29 | 250 | 0.013880 |
| 2023-02-03 | 250 | 0.013880 |
| 2020-12-29 | 249 | 0.013824 |
| 2021-05-16 | 249 | 0.013824 |
| 2020-12-03 | 249 | 0.013824 |
| 2022-12-30 | 249 | 0.013824 |
| 2020-12-15 | 249 | 0.013824 |
| 2022-06-24 | 248 | 0.013768 |
| 2022-10-28 | 248 | 0.013768 |
| 2022-10-07 | 247 | 0.013713 |
| 2021-04-16 | 247 | 0.013713 |
| 2020-12-18 | 246 | 0.013657 |
| 2021-04-09 | 245 | 0.013602 |
| 2021-12-29 | 245 | 0.013602 |
| 2023-01-21 | 245 | 0.013602 |
| 2022-02-18 | 245 | 0.013602 |
| 2022-07-10 | 244 | 0.013546 |
| 2021-11-16 | 244 | 0.013546 |
| 2023-05-25 | 243 | 0.013491 |
| 2022-07-08 | 243 | 0.013491 |
| 2022-09-24 | 242 | 0.013435 |
| 2023-03-14 | 242 | 0.013435 |
| 2022-10-19 | 241 | 0.013380 |
| 2023-02-13 | 241 | 0.013380 |
| 2021-12-15 | 241 | 0.013380 |
| 2022-11-15 | 240 | 0.013324 |
| 2023-04-22 | 240 | 0.013324 |
| 2022-12-01 | 240 | 0.013324 |
| 2021-05-06 | 239 | 0.013269 |
| 2022-02-16 | 238 | 0.013213 |
| 2022-05-27 | 238 | 0.013213 |
| 2023-01-28 | 237 | 0.013158 |
| 2023-06-23 | 237 | 0.013158 |
| 2021-12-16 | 237 | 0.013158 |
| 2022-02-11 | 237 | 0.013158 |
| 2021-06-04 | 237 | 0.013158 |
| 2023-04-18 | 237 | 0.013158 |
| 2023-01-11 | 237 | 0.013158 |
| 2022-04-14 | 237 | 0.013158 |
| 2021-10-03 | 236 | 0.013102 |
| 2023-04-13 | 235 | 0.013047 |
| 2023-06-01 | 235 | 0.013047 |
| 2020-12-31 | 235 | 0.013047 |
| 2022-08-10 | 234 | 0.012991 |
| 2023-02-05 | 234 | 0.012991 |
| 2023-04-28 | 234 | 0.012991 |
| 2020-12-14 | 233 | 0.012936 |
| 2023-06-14 | 233 | 0.012936 |
| 2021-12-31 | 233 | 0.012936 |
| 2022-01-01 | 233 | 0.012936 |
| 2022-09-09 | 232 | 0.012880 |
| 2022-02-26 | 232 | 0.012880 |
| 2021-05-26 | 230 | 0.012769 |
| 2023-06-30 | 230 | 0.012769 |
| 2023-01-13 | 230 | 0.012769 |
| 2022-05-20 | 230 | 0.012769 |
| 2022-06-01 | 230 | 0.012769 |
| 2021-05-11 | 229 | 0.012714 |
| 2022-11-19 | 229 | 0.012714 |
| 2022-06-21 | 229 | 0.012714 |
| 2022-11-22 | 228 | 0.012658 |
| 2021-12-01 | 228 | 0.012658 |
| 2021-09-30 | 228 | 0.012658 |
| 2022-08-17 | 227 | 0.012603 |
| 2020-12-23 | 227 | 0.012603 |
| 2023-06-03 | 227 | 0.012603 |
| 2023-05-09 | 227 | 0.012603 |
| 2023-06-17 | 227 | 0.012603 |
| 2023-01-20 | 226 | 0.012547 |
| 2021-12-25 | 226 | 0.012547 |
| 2021-05-22 | 225 | 0.012492 |
| 2022-11-08 | 225 | 0.012492 |
| 2021-09-23 | 225 | 0.012492 |
| 2023-05-10 | 224 | 0.012436 |
| 2022-02-17 | 224 | 0.012436 |
| 2022-10-25 | 223 | 0.012381 |
| 2021-02-03 | 223 | 0.012381 |
| 2021-10-16 | 223 | 0.012381 |
| 2022-04-22 | 223 | 0.012381 |
| 2021-10-08 | 223 | 0.012381 |
| 2021-11-11 | 222 | 0.012325 |
| 2022-06-11 | 222 | 0.012325 |
| 2022-10-26 | 221 | 0.012269 |
| 2021-07-09 | 221 | 0.012269 |
| 2022-09-17 | 221 | 0.012269 |
| 2021-10-22 | 220 | 0.012214 |
| 2021-10-23 | 220 | 0.012214 |
| 2021-11-23 | 220 | 0.012214 |
| 2023-03-11 | 220 | 0.012214 |
| 2021-02-23 | 219 | 0.012158 |
| 2022-10-09 | 219 | 0.012158 |
| 2022-03-20 | 219 | 0.012158 |
| 2022-07-13 | 219 | 0.012158 |
| 2022-02-22 | 218 | 0.012103 |
| 2022-02-08 | 218 | 0.012103 |
| 2021-02-06 | 218 | 0.012103 |
| 2021-09-29 | 217 | 0.012047 |
| 2022-01-13 | 217 | 0.012047 |
| 2021-09-16 | 216 | 0.011992 |
| 2022-03-30 | 216 | 0.011992 |
| 2022-11-29 | 216 | 0.011992 |
| 2022-05-14 | 215 | 0.011936 |
| 2022-10-04 | 215 | 0.011936 |
| 2022-12-29 | 215 | 0.011936 |
| 2022-12-31 | 214 | 0.011881 |
| 2021-10-15 | 214 | 0.011881 |
| 2023-07-06 | 214 | 0.011881 |
| 2020-10-15 | 213 | 0.011825 |
| 2022-06-16 | 213 | 0.011825 |
| 2023-05-27 | 212 | 0.011770 |
| 2021-05-21 | 212 | 0.011770 |
| 2021-11-24 | 211 | 0.011714 |
| 2021-09-24 | 211 | 0.011714 |
| 2020-10-30 | 211 | 0.011714 |
| 2022-02-12 | 210 | 0.011659 |
| 2022-01-27 | 210 | 0.011659 |
| 2023-05-18 | 209 | 0.011603 |
| 2022-09-16 | 209 | 0.011603 |
| 2022-02-23 | 209 | 0.011603 |
| 2021-01-09 | 209 | 0.011603 |
| 2021-08-04 | 208 | 0.011548 |
| 2020-10-20 | 208 | 0.011548 |
| 2020-11-28 | 208 | 0.011548 |
| 2020-12-16 | 207 | 0.011492 |
| 2023-05-07 | 207 | 0.011492 |
| 2023-06-06 | 207 | 0.011492 |
| 2021-05-18 | 206 | 0.011437 |
| 2020-12-08 | 206 | 0.011437 |
| 2022-07-16 | 206 | 0.011437 |
| 2022-11-12 | 206 | 0.011437 |
| 2022-01-10 | 206 | 0.011437 |
| 2022-11-06 | 205 | 0.011381 |
| 2021-10-26 | 205 | 0.011381 |
| 2021-09-15 | 205 | 0.011381 |
| 2022-01-29 | 205 | 0.011381 |
| 2022-10-22 | 205 | 0.011381 |
| 2022-05-08 | 205 | 0.011381 |
| 2023-03-21 | 204 | 0.011326 |
| 2022-09-22 | 204 | 0.011326 |
| 2021-04-23 | 204 | 0.011326 |
| 2021-10-19 | 204 | 0.011326 |
| 2021-11-27 | 203 | 0.011270 |
| 2022-05-31 | 202 | 0.011215 |
| 2022-12-24 | 202 | 0.011215 |
| 2020-10-08 | 202 | 0.011215 |
| 2020-10-10 | 202 | 0.011215 |
| 2020-11-13 | 200 | 0.011104 |
| 2022-09-10 | 200 | 0.011104 |
| 2021-05-28 | 200 | 0.011104 |
| 2020-12-12 | 199 | 0.011048 |
| 2021-12-10 | 199 | 0.011048 |
| 2022-01-21 | 199 | 0.011048 |
| 2021-04-27 | 199 | 0.011048 |
| 2023-05-19 | 198 | 0.010993 |
| 2022-07-29 | 198 | 0.010993 |
| 2023-04-25 | 198 | 0.010993 |
| 2023-06-08 | 197 | 0.010937 |
| 2021-08-11 | 197 | 0.010937 |
| 2022-01-12 | 197 | 0.010937 |
| 2023-01-17 | 197 | 0.010937 |
| 2022-04-15 | 197 | 0.010937 |
| 2021-01-26 | 197 | 0.010937 |
| 2022-08-11 | 197 | 0.010937 |
| 2021-04-21 | 196 | 0.010882 |
| 2022-04-05 | 195 | 0.010826 |
| 2023-03-18 | 195 | 0.010826 |
| 2021-02-04 | 195 | 0.010826 |
| 2022-02-19 | 195 | 0.010826 |
| 2023-01-24 | 195 | 0.010826 |
| 2021-06-26 | 194 | 0.010771 |
| 2020-10-01 | 194 | 0.010771 |
| 2020-10-14 | 193 | 0.010715 |
| 2022-07-12 | 193 | 0.010715 |
| 2021-02-26 | 193 | 0.010715 |
| 2022-05-17 | 193 | 0.010715 |
| 2022-01-18 | 191 | 0.010604 |
| 2021-12-08 | 190 | 0.010548 |
| 2023-06-24 | 190 | 0.010548 |
| 2022-04-13 | 189 | 0.010493 |
| 2021-06-11 | 189 | 0.010493 |
| 2021-06-12 | 188 | 0.010437 |
| 2021-02-16 | 188 | 0.010437 |
| 2021-06-19 | 188 | 0.010437 |
| 2023-05-23 | 188 | 0.010437 |
| 2021-11-06 | 188 | 0.010437 |
| 2022-04-27 | 188 | 0.010437 |
| 2022-07-30 | 187 | 0.010382 |
| 2022-07-27 | 187 | 0.010382 |
| 2022-08-24 | 186 | 0.010326 |
| 2021-06-30 | 185 | 0.010271 |
| 2021-11-10 | 185 | 0.010271 |
| 2023-04-01 | 185 | 0.010271 |
| 2023-01-05 | 184 | 0.010215 |
| 2021-04-28 | 183 | 0.010160 |
| 2022-05-21 | 183 | 0.010160 |
| 2022-02-01 | 183 | 0.010160 |
| 2022-11-03 | 183 | 0.010160 |
| 2021-11-20 | 183 | 0.010160 |
| 2022-06-30 | 183 | 0.010160 |
| 2021-03-30 | 182 | 0.010104 |
| 2021-06-24 | 182 | 0.010104 |
| 2021-10-21 | 182 | 0.010104 |
| 2022-04-26 | 181 | 0.010049 |
| 2022-05-26 | 181 | 0.010049 |
| 2021-06-15 | 181 | 0.010049 |
| 2021-09-17 | 180 | 0.009993 |
| 2020-11-21 | 180 | 0.009993 |
| 2020-11-19 | 180 | 0.009993 |
| 2021-10-09 | 180 | 0.009993 |
| 2022-12-06 | 179 | 0.009938 |
| 2021-09-10 | 179 | 0.009938 |
| 2020-11-04 | 178 | 0.009882 |
| 2020-12-01 | 178 | 0.009882 |
| 2022-07-23 | 178 | 0.009882 |
| 2021-04-20 | 178 | 0.009882 |
| 2021-07-23 | 178 | 0.009882 |
| 2021-07-16 | 178 | 0.009882 |
| 2020-10-27 | 177 | 0.009827 |
| 2021-01-23 | 177 | 0.009827 |
| 2021-09-08 | 177 | 0.009827 |
| 2020-10-16 | 176 | 0.009771 |
| 2022-09-13 | 176 | 0.009771 |
| 2021-05-05 | 176 | 0.009771 |
| 2022-09-27 | 176 | 0.009771 |
| 2021-05-15 | 175 | 0.009716 |
| 2022-07-19 | 175 | 0.009716 |
| 2023-05-30 | 175 | 0.009716 |
| 2022-10-29 | 174 | 0.009660 |
| 2021-07-14 | 174 | 0.009660 |
| 2021-01-29 | 174 | 0.009660 |
| 2021-12-05 | 172 | 0.009549 |
| 2022-02-25 | 172 | 0.009549 |
| 2021-01-31 | 172 | 0.009549 |
| 2022-12-08 | 172 | 0.009549 |
| 2022-07-03 | 172 | 0.009549 |
| 2021-10-27 | 171 | 0.009494 |
| 2022-04-21 | 171 | 0.009494 |
| 2023-05-06 | 170 | 0.009438 |
| 2023-06-27 | 170 | 0.009438 |
| 2021-12-11 | 170 | 0.009438 |
| 2023-06-13 | 170 | 0.009438 |
| 2023-05-16 | 170 | 0.009438 |
| 2022-12-13 | 170 | 0.009438 |
| 2021-03-12 | 170 | 0.009438 |
| 2021-04-30 | 169 | 0.009383 |
| 2021-09-28 | 169 | 0.009383 |
| 2020-11-26 | 169 | 0.009383 |
| 2022-08-03 | 168 | 0.009327 |
| 2021-09-18 | 168 | 0.009327 |
| 2021-02-13 | 168 | 0.009327 |
| 2021-03-19 | 167 | 0.009272 |
| 2021-07-31 | 167 | 0.009272 |
| 2021-11-09 | 166 | 0.009216 |
| 2021-07-13 | 166 | 0.009216 |
| 2020-11-14 | 166 | 0.009216 |
| 2021-05-04 | 166 | 0.009216 |
| 2021-02-09 | 166 | 0.009216 |
| 2021-05-01 | 166 | 0.009216 |
| 2021-11-26 | 165 | 0.009160 |
| 2021-10-05 | 165 | 0.009160 |
| 2021-10-30 | 165 | 0.009160 |
| 2020-12-21 | 165 | 0.009160 |
| 2022-06-28 | 164 | 0.009105 |
| 2020-11-24 | 164 | 0.009105 |
| 2021-10-28 | 163 | 0.009049 |
| 2021-02-12 | 162 | 0.008994 |
| 2020-10-31 | 162 | 0.008994 |
| 2021-06-16 | 161 | 0.008938 |
| 2022-08-31 | 161 | 0.008938 |
| 2022-08-05 | 161 | 0.008938 |
| 2020-11-10 | 161 | 0.008938 |
| 2021-04-13 | 160 | 0.008883 |
| 2023-04-29 | 160 | 0.008883 |
| 2023-06-10 | 160 | 0.008883 |
| 2021-10-06 | 159 | 0.008827 |
| 2021-02-20 | 159 | 0.008827 |
| 2021-10-20 | 159 | 0.008827 |
| 2023-06-20 | 158 | 0.008772 |
| 2021-01-22 | 158 | 0.008772 |
| 2022-04-23 | 158 | 0.008772 |
| 2021-09-25 | 158 | 0.008772 |
| 2021-07-21 | 158 | 0.008772 |
| 2021-07-10 | 157 | 0.008716 |
| 2021-03-17 | 156 | 0.008661 |
| 2021-03-26 | 156 | 0.008661 |
| 2020-11-25 | 155 | 0.008605 |
| 2022-07-26 | 155 | 0.008605 |
| 2022-06-25 | 154 | 0.008550 |
| 2021-05-19 | 154 | 0.008550 |
| 2022-02-09 | 153 | 0.008494 |
| 2021-03-13 | 153 | 0.008494 |
| 2020-11-18 | 152 | 0.008439 |
| 2020-12-06 | 152 | 0.008439 |
| 2022-01-25 | 151 | 0.008383 |
| 2021-09-03 | 151 | 0.008383 |
| 2021-03-24 | 150 | 0.008328 |
| 2020-10-28 | 150 | 0.008328 |
| 2021-01-16 | 150 | 0.008328 |
| 2022-06-18 | 150 | 0.008328 |
| 2021-03-31 | 149 | 0.008272 |
| 2022-01-22 | 148 | 0.008217 |
| 2021-05-25 | 148 | 0.008217 |
| 2021-07-27 | 148 | 0.008217 |
| 2021-10-12 | 147 | 0.008161 |
| 2021-03-09 | 147 | 0.008161 |
| 2021-09-11 | 147 | 0.008161 |
| 2021-07-20 | 146 | 0.008106 |
| 2022-01-26 | 145 | 0.008050 |
| 2021-02-27 | 145 | 0.008050 |
| 2021-04-24 | 145 | 0.008050 |
| 2021-10-04 | 144 | 0.007995 |
| 2021-01-15 | 144 | 0.007995 |
| 2022-08-12 | 143 | 0.007939 |
| 2022-06-03 | 142 | 0.007884 |
| 2023-07-01 | 141 | 0.007828 |
| 2023-05-20 | 141 | 0.007828 |
| 2022-11-05 | 141 | 0.007828 |
| 2022-09-20 | 141 | 0.007828 |
| 2021-01-08 | 141 | 0.007828 |
| 2020-11-12 | 140 | 0.007773 |
| 2021-03-16 | 139 | 0.007717 |
| 2023-05-03 | 138 | 0.007661 |
| 2021-08-10 | 138 | 0.007661 |
| 2020-10-17 | 137 | 0.007606 |
| 2021-01-27 | 136 | 0.007550 |
| 2021-08-18 | 136 | 0.007550 |
| 2021-04-07 | 135 | 0.007495 |
| 2020-10-23 | 135 | 0.007495 |
| 2022-05-04 | 135 | 0.007495 |
| 2022-08-23 | 135 | 0.007495 |
| 2021-02-19 | 135 | 0.007495 |
| 2022-07-06 | 132 | 0.007328 |
| 2020-11-11 | 132 | 0.007328 |
| 2021-09-14 | 132 | 0.007328 |
| 2022-01-11 | 131 | 0.007273 |
| 2022-08-13 | 130 | 0.007217 |
| 2021-07-07 | 129 | 0.007162 |
| 2021-07-17 | 129 | 0.007162 |
| 2022-12-27 | 128 | 0.007106 |
| 2023-03-29 | 128 | 0.007106 |
| 2022-08-09 | 128 | 0.007106 |
| 2021-07-24 | 126 | 0.006995 |
| 2020-07-16 | 125 | 0.006940 |
| 2022-06-07 | 123 | 0.006829 |
| 2020-12-10 | 123 | 0.006829 |
| 2021-10-14 | 122 | 0.006773 |
| 2021-03-20 | 121 | 0.006718 |
| 2022-09-21 | 121 | 0.006718 |
| 2021-04-10 | 118 | 0.006551 |
| 2020-10-21 | 117 | 0.006496 |
| 2022-05-25 | 115 | 0.006385 |
| 2020-11-05 | 114 | 0.006329 |
| 2020-10-09 | 113 | 0.006274 |
| 2021-03-01 | 111 | 0.006163 |
| 2022-08-30 | 111 | 0.006163 |
| 2021-03-10 | 108 | 0.005996 |
| 2023-03-04 | 108 | 0.005996 |
| 2022-10-05 | 107 | 0.005940 |
| 2021-03-23 | 107 | 0.005940 |
| 2021-11-05 | 105 | 0.005829 |
| 2022-04-16 | 104 | 0.005774 |
| 2022-01-14 | 103 | 0.005718 |
| 2021-08-19 | 103 | 0.005718 |
| 2021-01-12 | 102 | 0.005663 |
| 2020-12-05 | 102 | 0.005663 |
| 2021-01-07 | 100 | 0.005552 |
| 2021-08-06 | 99 | 0.005496 |
| 2020-09-27 | 99 | 0.005496 |
| 2021-08-13 | 97 | 0.005385 |
| 2021-11-03 | 95 | 0.005274 |
| 2022-09-01 | 95 | 0.005274 |
| 2021-04-03 | 94 | 0.005219 |
| 2022-08-18 | 94 | 0.005219 |
| 2023-04-11 | 93 | 0.005163 |
| 2022-04-19 | 92 | 0.005108 |
| 2020-09-15 | 91 | 0.005052 |
| 2021-08-26 | 90 | 0.004997 |
| 2021-08-31 | 89 | 0.004941 |
| 2021-09-01 | 89 | 0.004941 |
| 2021-01-13 | 87 | 0.004830 |
| 2022-03-28 | 86 | 0.004775 |
| 2022-10-03 | 86 | 0.004775 |
| 2021-12-20 | 85 | 0.004719 |
| 2022-08-25 | 84 | 0.004664 |
| 2020-12-28 | 84 | 0.004664 |
| 2020-12-20 | 84 | 0.004664 |
| 2022-08-27 | 82 | 0.004552 |
| 2022-03-21 | 81 | 0.004497 |
| 2023-01-01 | 81 | 0.004497 |
| 2021-08-01 | 81 | 0.004497 |
| 2020-10-25 | 80 | 0.004441 |
| 2023-04-16 | 79 | 0.004386 |
| 2020-10-04 | 79 | 0.004386 |
| 2021-08-17 | 76 | 0.004219 |
| 2021-12-27 | 75 | 0.004164 |
| 2022-01-03 | 74 | 0.004108 |
| 2021-08-24 | 73 | 0.004053 |
| 2023-06-04 | 73 | 0.004053 |
| 2020-12-27 | 73 | 0.004053 |
| 2020-11-08 | 72 | 0.003997 |
| 2021-11-04 | 72 | 0.003997 |
| 2022-05-18 | 72 | 0.003997 |
| 2021-08-25 | 71 | 0.003942 |
| 2021-08-28 | 71 | 0.003942 |
| 2021-08-20 | 68 | 0.003775 |
| 2021-08-14 | 68 | 0.003775 |
| 2023-04-17 | 68 | 0.003775 |
| 2023-02-19 | 67 | 0.003720 |
| 2021-12-04 | 67 | 0.003720 |
| 2021-04-06 | 67 | 0.003720 |
| 2021-08-27 | 66 | 0.003664 |
| 2022-08-26 | 65 | 0.003609 |
| 2021-08-12 | 65 | 0.003609 |
| 2023-02-20 | 65 | 0.003609 |
| 2021-09-21 | 63 | 0.003498 |
| 2020-12-09 | 63 | 0.003498 |
| 2021-12-26 | 62 | 0.003442 |
| 2022-05-23 | 61 | 0.003387 |
| 2022-08-19 | 61 | 0.003387 |
| 2021-08-21 | 59 | 0.003276 |
| 2022-07-05 | 58 | 0.003220 |
| 2020-12-26 | 58 | 0.003220 |
| 2022-01-30 | 56 | 0.003109 |
| 2020-12-07 | 55 | 0.003053 |
| 2022-08-20 | 55 | 0.003053 |
| 2021-03-03 | 55 | 0.003053 |
| 2022-12-18 | 54 | 0.002998 |
| 2021-01-05 | 51 | 0.002831 |
| 2022-12-07 | 48 | 0.002665 |
| 2022-02-28 | 47 | 0.002609 |
| 2023-02-04 | 46 | 0.002554 |
| 2022-12-25 | 46 | 0.002554 |
| 2022-11-04 | 45 | 0.002498 |
| 2022-12-19 | 45 | 0.002498 |
| 2020-09-26 | 45 | 0.002498 |
| 2021-03-05 | 45 | 0.002498 |
| 2020-10-07 | 42 | 0.002332 |
| 2020-12-13 | 41 | 0.002276 |
| 2022-02-07 | 40 | 0.002221 |
| 2020-11-07 | 40 | 0.002221 |
| 2020-11-22 | 39 | 0.002165 |
| 2022-12-26 | 38 | 0.002110 |
| 2021-12-07 | 38 | 0.002110 |
| 2020-10-05 | 37 | 0.002054 |
| 2023-01-08 | 36 | 0.001999 |
| 2021-02-01 | 34 | 0.001888 |
| 2021-01-24 | 34 | 0.001888 |
| 2021-07-12 | 33 | 0.001832 |
| 2022-04-11 | 33 | 0.001832 |
| 2022-12-12 | 33 | 0.001832 |
| 2021-02-07 | 32 | 0.001777 |
| 2023-04-09 | 32 | 0.001777 |
| 2020-10-26 | 32 | 0.001777 |
| 2023-02-27 | 32 | 0.001777 |
| 2020-11-23 | 30 | 0.001666 |
| 2023-06-25 | 30 | 0.001666 |
| 2021-01-25 | 29 | 0.001610 |
| 2020-11-30 | 29 | 0.001610 |
| 2022-12-09 | 29 | 0.001610 |
| 2022-04-04 | 29 | 0.001610 |
| 2021-12-12 | 28 | 0.001555 |
| 2020-11-03 | 28 | 0.001555 |
| 2021-04-04 | 28 | 0.001555 |
| 2022-01-24 | 28 | 0.001555 |
| 2021-08-09 | 27 | 0.001499 |
| 2021-02-08 | 27 | 0.001499 |
| 2020-11-01 | 27 | 0.001499 |
| 2021-03-29 | 27 | 0.001499 |
| 2020-11-16 | 27 | 0.001499 |
| 2021-08-16 | 27 | 0.001499 |
| 2023-02-26 | 26 | 0.001443 |
| 2020-11-09 | 26 | 0.001443 |
| 2022-11-20 | 26 | 0.001443 |
| 2022-09-05 | 26 | 0.001443 |
| 2021-12-13 | 25 | 0.001388 |
| 2021-01-30 | 25 | 0.001388 |
| 2022-04-10 | 25 | 0.001388 |
| 2022-04-24 | 25 | 0.001388 |
| 2022-01-17 | 25 | 0.001388 |
| 2022-12-05 | 25 | 0.001388 |
| 2021-01-10 | 24 | 0.001332 |
| 2022-01-23 | 24 | 0.001332 |
| 2021-01-11 | 23 | 0.001277 |
| 2021-02-14 | 23 | 0.001277 |
| 2021-11-02 | 23 | 0.001277 |
| 2021-10-18 | 23 | 0.001277 |
| 2020-11-15 | 23 | 0.001277 |
| 2021-08-23 | 23 | 0.001277 |
| 2021-02-15 | 22 | 0.001221 |
| 2021-09-12 | 22 | 0.001221 |
| 2021-04-26 | 22 | 0.001221 |
| 2020-11-29 | 21 | 0.001166 |
| 2022-01-31 | 21 | 0.001166 |
| 2021-04-19 | 21 | 0.001166 |
| 2021-02-22 | 21 | 0.001166 |
| 2022-02-13 | 21 | 0.001166 |
| 2021-06-06 | 21 | 0.001166 |
| 2021-08-22 | 20 | 0.001110 |
| 2021-05-10 | 20 | 0.001110 |
| 2023-03-27 | 20 | 0.001110 |
| 2022-10-31 | 20 | 0.001110 |
| 2022-02-27 | 19 | 0.001055 |
| 2021-08-08 | 19 | 0.001055 |
| 2022-06-06 | 19 | 0.001055 |
| 2022-11-27 | 19 | 0.001055 |
| 2022-02-04 | 19 | 0.001055 |
| 2021-05-07 | 19 | 0.001055 |
| 2022-11-21 | 19 | 0.001055 |
| 2021-07-04 | 19 | 0.001055 |
| 2021-11-22 | 18 | 0.000999 |
| 2021-11-29 | 18 | 0.000999 |
| 2021-04-25 | 18 | 0.000999 |
| 2021-04-18 | 18 | 0.000999 |
| 2021-06-27 | 18 | 0.000999 |
| 2021-05-03 | 18 | 0.000999 |
| 2022-05-22 | 18 | 0.000999 |
| 2021-01-17 | 18 | 0.000999 |
| 2020-10-13 | 18 | 0.000999 |
| 2021-10-17 | 17 | 0.000944 |
| 2023-05-28 | 17 | 0.000944 |
| 2021-06-07 | 17 | 0.000944 |
| 2021-08-05 | 17 | 0.000944 |
| 2022-04-18 | 17 | 0.000944 |
| 2022-05-05 | 17 | 0.000944 |
| 2023-03-13 | 17 | 0.000944 |
| 2022-05-01 | 17 | 0.000944 |
| 2022-05-29 | 17 | 0.000944 |
| 2021-11-15 | 17 | 0.000944 |
| 2022-04-17 | 17 | 0.000944 |
| 2021-03-21 | 16 | 0.000888 |
| 2020-09-28 | 16 | 0.000888 |
| 2022-06-19 | 16 | 0.000888 |
| 2021-08-30 | 16 | 0.000888 |
| 2021-06-20 | 16 | 0.000888 |
| 2021-05-24 | 16 | 0.000888 |
| 2021-12-09 | 16 | 0.000888 |
| 2021-09-06 | 16 | 0.000888 |
| 2021-05-17 | 16 | 0.000888 |
| 2021-11-14 | 15 | 0.000833 |
| 2021-06-14 | 15 | 0.000833 |
| 2023-01-15 | 15 | 0.000833 |
| 2023-01-30 | 15 | 0.000833 |
| 2020-04-11 | 15 | 0.000833 |
| 2023-04-03 | 15 | 0.000833 |
| 2021-02-21 | 15 | 0.000833 |
| 2022-02-20 | 15 | 0.000833 |
| 2022-12-11 | 15 | 0.000833 |
| 2021-04-05 | 15 | 0.000833 |
| 2021-09-26 | 15 | 0.000833 |
| 2022-05-15 | 15 | 0.000833 |
| 2021-04-12 | 15 | 0.000833 |
| 2021-03-07 | 15 | 0.000833 |
| 2022-08-16 | 15 | 0.000833 |
| 2022-10-30 | 15 | 0.000833 |
| 2022-05-16 | 15 | 0.000833 |
| 2023-01-23 | 14 | 0.000777 |
| 2021-08-29 | 14 | 0.000777 |
| 2020-10-18 | 14 | 0.000777 |
| 2023-06-19 | 14 | 0.000777 |
| 2022-05-30 | 14 | 0.000777 |
| 2023-06-12 | 14 | 0.000777 |
| 2022-07-17 | 14 | 0.000777 |
| 2022-06-13 | 14 | 0.000777 |
| 2021-05-30 | 14 | 0.000777 |
| 2021-02-28 | 14 | 0.000777 |
| 2021-08-15 | 14 | 0.000777 |
| 2021-01-18 | 13 | 0.000722 |
| 2022-10-24 | 13 | 0.000722 |
| 2023-05-02 | 13 | 0.000722 |
| 2021-11-28 | 13 | 0.000722 |
| 2022-05-09 | 13 | 0.000722 |
| 2021-07-25 | 13 | 0.000722 |
| 2021-06-21 | 13 | 0.000722 |
| 2022-11-13 | 13 | 0.000722 |
| 2021-10-31 | 13 | 0.000722 |
| 2023-03-12 | 13 | 0.000722 |
| 2022-08-08 | 13 | 0.000722 |
| 2022-06-20 | 13 | 0.000722 |
| 2021-03-08 | 13 | 0.000722 |
| 2021-11-21 | 13 | 0.000722 |
| 2021-09-13 | 13 | 0.000722 |
| 2022-01-16 | 13 | 0.000722 |
| 2023-04-24 | 12 | 0.000666 |
| 2021-03-22 | 12 | 0.000666 |
| 2021-10-13 | 12 | 0.000666 |
| 2021-05-31 | 12 | 0.000666 |
| 2023-06-05 | 12 | 0.000666 |
| 2023-05-29 | 12 | 0.000666 |
| 2022-11-07 | 12 | 0.000666 |
| 2022-02-14 | 12 | 0.000666 |
| 2023-04-30 | 12 | 0.000666 |
| 2022-08-22 | 12 | 0.000666 |
| 2023-03-19 | 12 | 0.000666 |
| 2022-10-23 | 12 | 0.000666 |
| 2023-01-16 | 12 | 0.000666 |
| 2022-04-25 | 11 | 0.000611 |
| 2021-12-06 | 11 | 0.000611 |
| 2020-10-02 | 11 | 0.000611 |
| 2021-09-20 | 11 | 0.000611 |
| 2023-04-08 | 11 | 0.000611 |
| 2021-03-15 | 11 | 0.000611 |
| 2021-10-11 | 11 | 0.000611 |
| 2021-07-11 | 11 | 0.000611 |
| 2022-06-26 | 11 | 0.000611 |
| 2021-11-08 | 11 | 0.000611 |
| 2021-05-09 | 11 | 0.000611 |
| 2023-07-09 | 11 | 0.000611 |
| 2023-01-29 | 11 | 0.000611 |
| 2022-09-12 | 11 | 0.000611 |
| 2021-04-11 | 11 | 0.000611 |
| 2023-04-10 | 11 | 0.000611 |
| 2023-05-14 | 11 | 0.000611 |
| 2022-07-25 | 11 | 0.000611 |
| 2021-10-10 | 11 | 0.000611 |
| 2023-03-20 | 10 | 0.000555 |
| 2022-08-29 | 10 | 0.000555 |
| 2021-07-19 | 10 | 0.000555 |
| 2023-04-23 | 10 | 0.000555 |
| 2023-07-02 | 10 | 0.000555 |
| 2023-01-22 | 10 | 0.000555 |
| 2022-09-25 | 10 | 0.000555 |
| 2022-02-21 | 10 | 0.000555 |
| 2022-10-17 | 9 | 0.000500 |
| 2022-07-18 | 9 | 0.000500 |
| 2021-06-13 | 9 | 0.000500 |
| 2020-10-19 | 9 | 0.000500 |
| 2023-01-09 | 9 | 0.000500 |
| 2022-11-28 | 9 | 0.000500 |
| 2022-08-15 | 9 | 0.000500 |
| 2022-09-19 | 9 | 0.000500 |
| 2021-09-27 | 9 | 0.000500 |
| 2023-07-03 | 9 | 0.000500 |
| 2021-11-01 | 9 | 0.000500 |
| 2022-06-12 | 8 | 0.000444 |
| 2022-07-24 | 8 | 0.000444 |
| 2020-10-11 | 8 | 0.000444 |
| 2021-05-23 | 8 | 0.000444 |
| 2022-11-14 | 8 | 0.000444 |
| 2022-08-01 | 8 | 0.000444 |
| 2021-10-25 | 8 | 0.000444 |
| 2021-07-05 | 8 | 0.000444 |
| 2021-10-24 | 8 | 0.000444 |
| 2021-07-26 | 8 | 0.000444 |
| 2023-05-15 | 7 | 0.000389 |
| 2022-08-14 | 7 | 0.000389 |
| 2023-06-18 | 7 | 0.000389 |
| 2022-07-31 | 7 | 0.000389 |
| 2022-06-27 | 7 | 0.000389 |
| 2021-09-19 | 7 | 0.000389 |
| 2021-03-14 | 7 | 0.000389 |
| 2021-05-14 | 7 | 0.000389 |
| 2022-09-11 | 7 | 0.000389 |
| 2022-10-16 | 7 | 0.000389 |
| 2021-06-28 | 7 | 0.000389 |
| 2022-09-26 | 7 | 0.000389 |
| 2023-05-22 | 6 | 0.000333 |
| 2023-06-26 | 6 | 0.000333 |
| 2021-07-18 | 6 | 0.000333 |
| 2022-10-13 | 5 | 0.000278 |
| 2022-09-18 | 5 | 0.000278 |
| 2022-08-21 | 5 | 0.000278 |
| 2022-08-28 | 5 | 0.000278 |
| 2021-09-05 | 5 | 0.000278 |
| 2023-05-01 | 5 | 0.000278 |
| 2023-05-21 | 5 | 0.000278 |
| 2022-05-02 | 4 | 0.000222 |
| 2023-06-11 | 4 | 0.000222 |
| 2021-10-02 | 3 | 0.000167 |
| 2022-11-02 | 2 | 0.000111 |
| 2021-07-06 | 2 | 0.000111 |
| 2023-07-04 | 2 | 0.000111 |
| 2022-07-04 | 2 | 0.000111 |
| 2022-10-08 | 1 | 0.000056 |
| 2022-08-07 | 1 | 0.000056 |
| 2021-08-03 | 1 | 0.000056 |
| 2022-01-04 | 1 | 0.000056 |
| 2023-06-02 | 1 | 0.000056 |
# Vamos a realizar analisis por cada variable
var = "msf_datefirstdonation__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstdonation__c es 1195087. Lo que supone un 66.26785012246184% El nº de vacios para la variable msf_datefirstdonation__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2017-05-16 | 11149 | 1.832716 |
| 2010-02-01 | 5502 | 0.904440 |
| 2017-12-01 | 4984 | 0.819289 |
| 2020-07-01 | 4452 | 0.731837 |
| 2010-01-15 | 3777 | 0.620878 |
| ... | ... | ... |
| 2007-01-28 | 1 | 0.000164 |
| 1991-03-19 | 1 | 0.000164 |
| 1990-02-13 | 1 | 0.000164 |
| 1992-01-28 | 1 | 0.000164 |
| 2013-04-14 | 1 | 0.000164 |
11049 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_datefirstrecurringdonorquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstrecurringdonorquota__c es 858069. Lo que supone un 47.58012419742722% El nº de vacios para la variable msf_datefirstrecurringdonorquota__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2006-01-05 | 9932 | 1.050616 |
| 2011-01-03 | 9921 | 1.049453 |
| 2009-01-02 | 9654 | 1.021209 |
| 2021-01-05 | 8946 | 0.946316 |
| 2014-11-03 | 8567 | 0.906225 |
| 2015-01-02 | 8523 | 0.901571 |
| 2005-02-04 | 8472 | 0.896176 |
| 2014-12-02 | 8309 | 0.878934 |
| 2005-01-04 | 8185 | 0.865817 |
| 2012-01-02 | 8034 | 0.849844 |
| 2016-07-01 | 7977 | 0.843814 |
| 2004-02-01 | 7780 | 0.822976 |
| 2004-01-01 | 7525 | 0.796001 |
| 2015-10-01 | 7445 | 0.787539 |
| 2007-01-04 | 6987 | 0.739091 |
| 2016-01-04 | 6778 | 0.716983 |
| 2017-04-03 | 6649 | 0.703337 |
| 2010-01-04 | 6590 | 0.697096 |
| 2003-01-01 | 6482 | 0.685672 |
| 2015-11-03 | 6357 | 0.672449 |
| 2013-01-02 | 6285 | 0.664833 |
| 2016-04-01 | 6213 | 0.657217 |
| 2015-12-02 | 6209 | 0.656794 |
| 2003-03-01 | 6125 | 0.647908 |
| 2017-06-01 | 6057 | 0.640715 |
| 2016-08-01 | 6024 | 0.637224 |
| 2015-03-02 | 5930 | 0.627281 |
| 2015-02-02 | 5913 | 0.625483 |
| 2014-05-05 | 5836 | 0.617337 |
| 2016-10-03 | 5733 | 0.606442 |
| 2016-12-01 | 5723 | 0.605384 |
| 2015-04-01 | 5646 | 0.597239 |
| 2015-05-04 | 5559 | 0.588036 |
| 2017-07-03 | 5550 | 0.587084 |
| 2016-05-02 | 5498 | 0.581584 |
| 2016-11-02 | 5485 | 0.580208 |
| 2017-12-04 | 5456 | 0.577141 |
| 2010-02-01 | 5447 | 0.576189 |
| 2016-06-01 | 5400 | 0.571217 |
| 2017-01-02 | 5374 | 0.568467 |
| 2015-06-02 | 5328 | 0.563601 |
| 2006-02-03 | 5327 | 0.563495 |
| 2016-03-01 | 5319 | 0.562649 |
| 2017-08-01 | 5305 | 0.561168 |
| 2017-03-02 | 5278 | 0.558312 |
| 2009-02-03 | 5192 | 0.549215 |
| 2016-02-01 | 5139 | 0.543608 |
| 2014-01-02 | 5138 | 0.543502 |
| 2015-08-03 | 5125 | 0.542127 |
| 2014-08-01 | 4955 | 0.524144 |
| 2017-05-02 | 4884 | 0.516634 |
| 2014-06-05 | 4880 | 0.516211 |
| 2015-07-01 | 4823 | 0.510181 |
| 2017-02-02 | 4793 | 0.507008 |
| 2018-01-03 | 4793 | 0.507008 |
| 2009-12-02 | 4788 | 0.506479 |
| 2018-02-01 | 4744 | 0.501825 |
| 2018-06-01 | 4697 | 0.496853 |
| 2010-12-02 | 4667 | 0.493680 |
| 2018-03-01 | 4629 | 0.489660 |
| 2014-10-02 | 4554 | 0.481726 |
| 2014-04-02 | 4549 | 0.481197 |
| 2000-02-01 | 4504 | 0.476437 |
| 2012-02-01 | 4426 | 0.468186 |
| 2007-02-05 | 4376 | 0.462897 |
| 2013-02-01 | 4356 | 0.460782 |
| 2017-09-01 | 4342 | 0.459301 |
| 2011-12-01 | 4336 | 0.458666 |
| 2014-07-02 | 4277 | 0.452425 |
| 2018-07-02 | 4214 | 0.445761 |
| 2017-11-02 | 4202 | 0.444491 |
| 2014-02-03 | 4167 | 0.440789 |
| 2022-04-02 | 4157 | 0.439731 |
| 2018-08-01 | 4146 | 0.438568 |
| 2018-12-03 | 4109 | 0.434654 |
| 2011-02-01 | 4044 | 0.427778 |
| 2017-10-02 | 4002 | 0.423335 |
| 2018-04-03 | 3975 | 0.420479 |
| 2018-11-02 | 3934 | 0.416142 |
| 2008-12-01 | 3868 | 0.409161 |
| 2013-12-02 | 3847 | 0.406939 |
| 2005-03-04 | 3824 | 0.404506 |
| 2013-11-04 | 3805 | 0.402496 |
| 2019-12-02 | 3789 | 0.400804 |
| 2008-01-03 | 3781 | 0.399958 |
| 2020-02-03 | 3769 | 0.398688 |
| 2000-01-01 | 3719 | 0.393399 |
| 2014-03-03 | 3718 | 0.393293 |
| 2019-01-02 | 3698 | 0.391178 |
| 1994-10-01 | 3689 | 0.390226 |
| 2008-02-04 | 3668 | 0.388004 |
| 2019-11-04 | 3644 | 0.385466 |
| 2016-09-01 | 3614 | 0.382292 |
| 2011-04-01 | 3614 | 0.382292 |
| 2006-12-02 | 3592 | 0.379965 |
| 2020-03-02 | 3576 | 0.378273 |
| 2019-06-03 | 3564 | 0.377003 |
| 2021-04-02 | 3550 | 0.375522 |
| 2021-03-02 | 3533 | 0.373724 |
| 2022-12-02 | 3531 | 0.373512 |
| 2020-01-02 | 3525 | 0.372878 |
| 2018-05-03 | 3520 | 0.372349 |
| 2013-05-02 | 3516 | 0.371926 |
| 2014-09-03 | 3514 | 0.371714 |
| 2021-07-02 | 3491 | 0.369281 |
| 2019-08-01 | 3489 | 0.369070 |
| 2013-08-02 | 3472 | 0.367271 |
| 2019-05-02 | 3458 | 0.365790 |
| 2019-04-01 | 3450 | 0.364944 |
| 2021-06-02 | 3422 | 0.361982 |
| 2019-07-01 | 3411 | 0.360819 |
| 2023-03-02 | 3388 | 0.358386 |
| 2022-07-05 | 3378 | 0.357328 |
| 2019-02-01 | 3371 | 0.356588 |
| 2013-06-03 | 3351 | 0.354472 |
| 2001-03-01 | 3338 | 0.353097 |
| 2011-03-01 | 3329 | 0.352145 |
| 1995-02-01 | 3309 | 0.350029 |
| 2023-04-04 | 3299 | 0.348971 |
| 2011-08-02 | 3284 | 0.347385 |
| 2018-10-02 | 3267 | 0.345586 |
| 2012-12-03 | 3224 | 0.341038 |
| 2023-06-02 | 3214 | 0.339980 |
| 2013-03-01 | 3173 | 0.335643 |
| 2019-03-01 | 3145 | 0.332681 |
| 2007-12-02 | 3138 | 0.331941 |
| 2013-04-02 | 3135 | 0.331623 |
| 2022-11-03 | 3133 | 0.331412 |
| 2019-10-02 | 3129 | 0.330989 |
| 2012-11-02 | 3101 | 0.328027 |
| 2015-09-01 | 3095 | 0.327392 |
| 2023-07-04 | 3090 | 0.326863 |
| 2022-06-02 | 3090 | 0.326863 |
| 2021-10-02 | 3081 | 0.325911 |
| 2013-07-01 | 3057 | 0.323372 |
| 2001-02-01 | 3052 | 0.322843 |
| 2021-12-02 | 3037 | 0.321257 |
| 2010-08-02 | 3034 | 0.320939 |
| 2021-05-04 | 3031 | 0.320622 |
| 2023-02-02 | 3003 | 0.317660 |
| 2004-03-01 | 2977 | 0.314910 |
| 2005-12-03 | 2966 | 0.313746 |
| 2022-10-04 | 2932 | 0.310150 |
| 2012-08-01 | 2918 | 0.308669 |
| 2021-11-03 | 2889 | 0.305601 |
| 2022-01-04 | 2796 | 0.295763 |
| 2022-08-02 | 2788 | 0.294917 |
| 1998-03-01 | 2785 | 0.294600 |
| 2010-03-01 | 2774 | 0.293436 |
| 2009-03-03 | 2766 | 0.292590 |
| 2011-11-02 | 2764 | 0.292378 |
| 2013-10-02 | 2744 | 0.290263 |
| 2018-09-03 | 2741 | 0.289946 |
| 2021-08-03 | 2733 | 0.289099 |
| 2023-01-03 | 2726 | 0.288359 |
| 2012-03-01 | 2704 | 0.286032 |
| 1999-01-01 | 2671 | 0.282541 |
| 2021-02-02 | 2665 | 0.281906 |
| 2022-03-02 | 2663 | 0.281695 |
| 1994-02-01 | 2653 | 0.280637 |
| 2012-06-04 | 2649 | 0.280214 |
| 2022-05-03 | 2647 | 0.280002 |
| 2012-04-02 | 2627 | 0.277886 |
| 2011-05-02 | 2597 | 0.274713 |
| 2013-09-02 | 2590 | 0.273973 |
| 2022-02-02 | 2538 | 0.268472 |
| 2008-08-08 | 2535 | 0.268155 |
| 2020-04-02 | 2480 | 0.262337 |
| 2002-01-01 | 2476 | 0.261914 |
| 2010-04-01 | 2454 | 0.259586 |
| 2012-07-02 | 2451 | 0.259269 |
| 2011-07-01 | 2432 | 0.257259 |
| 2006-11-03 | 2415 | 0.255461 |
| 2011-06-01 | 2406 | 0.254509 |
| 2010-07-01 | 2364 | 0.250066 |
| 2008-04-04 | 2363 | 0.249960 |
| 2023-05-03 | 2331 | 0.246575 |
| 2020-05-03 | 2326 | 0.246046 |
| 2007-04-02 | 2315 | 0.244883 |
| 2007-03-02 | 2312 | 0.244566 |
| 2011-09-02 | 2278 | 0.240969 |
| 2011-10-04 | 2247 | 0.237690 |
| 2002-12-01 | 2244 | 0.237372 |
| 1999-02-01 | 2212 | 0.233987 |
| 2012-05-03 | 2203 | 0.233035 |
| 2008-03-03 | 2103 | 0.222457 |
| 2006-03-03 | 2090 | 0.221082 |
| 2009-04-02 | 2086 | 0.220659 |
| 2009-07-02 | 2080 | 0.220024 |
| 2012-10-01 | 2029 | 0.214630 |
| 2008-06-02 | 2025 | 0.214206 |
| 2010-06-02 | 2020 | 0.213677 |
| 2004-12-05 | 2000 | 0.211562 |
| 1996-02-01 | 1978 | 0.209235 |
| 2006-04-03 | 1971 | 0.208494 |
| 2007-10-04 | 1948 | 0.206061 |
| 2019-09-02 | 1913 | 0.202359 |
| 2001-01-01 | 1885 | 0.199397 |
| 2007-05-04 | 1875 | 0.198339 |
| 2007-09-03 | 1874 | 0.198233 |
| 2010-10-04 | 1874 | 0.198233 |
| 2007-07-04 | 1851 | 0.195800 |
| 2020-08-03 | 1830 | 0.193579 |
| 2005-11-03 | 1805 | 0.190935 |
| 1994-07-01 | 1800 | 0.190406 |
| 2003-12-01 | 1795 | 0.189877 |
| 2010-05-03 | 1767 | 0.186915 |
| 2005-08-02 | 1758 | 0.185963 |
| 2010-11-02 | 1716 | 0.181520 |
| 2009-08-03 | 1714 | 0.181309 |
| 2009-06-04 | 1707 | 0.180568 |
| 2009-10-02 | 1699 | 0.179722 |
| 2008-07-04 | 1689 | 0.178664 |
| 2009-05-04 | 1670 | 0.176654 |
| 2005-06-03 | 1660 | 0.175596 |
| 2006-06-02 | 1649 | 0.174433 |
| 1997-02-01 | 1647 | 0.174221 |
| 2008-05-02 | 1629 | 0.172317 |
| 2020-06-02 | 1626 | 0.172000 |
| 2005-07-04 | 1625 | 0.171894 |
| 2007-08-02 | 1584 | 0.167557 |
| 1998-02-01 | 1546 | 0.163537 |
| 2006-07-03 | 1528 | 0.161633 |
| 2009-09-02 | 1514 | 0.160152 |
| 2008-09-01 | 1513 | 0.160047 |
| 2009-11-02 | 1506 | 0.159306 |
| 1995-04-01 | 1480 | 0.156556 |
| 2000-03-01 | 1436 | 0.151901 |
| 2007-11-02 | 1434 | 0.151690 |
| 1994-01-01 | 1422 | 0.150420 |
| 2012-09-03 | 1422 | 0.150420 |
| 2022-09-02 | 1403 | 0.148411 |
| 2021-09-02 | 1394 | 0.147459 |
| 2020-07-01 | 1384 | 0.146401 |
| 2005-04-04 | 1365 | 0.144391 |
| 1992-11-01 | 1352 | 0.143016 |
| 1998-01-01 | 1348 | 0.142593 |
| 1994-09-01 | 1327 | 0.140371 |
| 2008-10-02 | 1293 | 0.136775 |
| 2008-11-03 | 1292 | 0.136669 |
| 2010-09-02 | 1286 | 0.136034 |
| 2005-05-04 | 1242 | 0.131380 |
| 2007-06-05 | 1226 | 0.129687 |
| 2020-09-01 | 1223 | 0.129370 |
| 2005-09-02 | 1180 | 0.124821 |
| 1995-03-01 | 1169 | 0.123658 |
| 2006-08-02 | 1121 | 0.118580 |
| 1999-06-01 | 1076 | 0.113820 |
| 2005-10-03 | 1055 | 0.111599 |
| 2006-05-04 | 973 | 0.102925 |
| 1999-03-01 | 960 | 0.101550 |
| 1997-01-01 | 930 | 0.098376 |
| 2002-04-01 | 880 | 0.093087 |
| 2002-02-01 | 878 | 0.092876 |
| 2004-04-01 | 875 | 0.092558 |
| 1994-03-01 | 866 | 0.091606 |
| 2006-09-04 | 862 | 0.091183 |
| 2003-11-01 | 850 | 0.089914 |
| 1995-01-01 | 839 | 0.088750 |
| 2003-06-01 | 830 | 0.087798 |
| 2006-10-02 | 826 | 0.087375 |
| 1995-07-01 | 821 | 0.086846 |
| 2003-04-01 | 795 | 0.084096 |
| 2002-05-01 | 780 | 0.082509 |
| 2004-11-04 | 769 | 0.081346 |
| 2001-04-01 | 756 | 0.079970 |
| 2000-05-01 | 744 | 0.078701 |
| 1999-07-01 | 710 | 0.075104 |
| 1994-04-01 | 691 | 0.073095 |
| 2003-08-01 | 683 | 0.072248 |
| 2004-05-01 | 667 | 0.070556 |
| 2004-06-01 | 662 | 0.070027 |
| 1992-12-01 | 662 | 0.070027 |
| 1995-10-01 | 661 | 0.069921 |
| 1996-04-01 | 651 | 0.068863 |
| 1999-05-01 | 643 | 0.068017 |
| 2000-04-01 | 634 | 0.067065 |
| 2001-08-01 | 617 | 0.065267 |
| 2004-08-01 | 609 | 0.064421 |
| 1999-12-01 | 591 | 0.062517 |
| 2020-12-02 | 589 | 0.062305 |
| 1998-04-01 | 577 | 0.061036 |
| 1996-12-01 | 573 | 0.060612 |
| 2003-05-01 | 555 | 0.058708 |
| 1998-09-01 | 549 | 0.058074 |
| 1998-12-01 | 531 | 0.056170 |
| 1994-06-01 | 524 | 0.055429 |
| 2000-01-13 | 516 | 0.054583 |
| 2001-12-01 | 513 | 0.054266 |
| 1996-01-01 | 510 | 0.053948 |
| 2001-07-01 | 505 | 0.053419 |
| 2004-10-06 | 499 | 0.052785 |
| 1998-11-01 | 492 | 0.052044 |
| 2004-07-01 | 490 | 0.051833 |
| 1993-11-01 | 489 | 0.051727 |
| 2002-11-01 | 485 | 0.051304 |
| 1996-03-01 | 477 | 0.050458 |
| 1996-06-01 | 476 | 0.050352 |
| 1995-06-01 | 466 | 0.049294 |
| 2002-08-01 | 463 | 0.048977 |
| 2003-10-01 | 455 | 0.048130 |
| 1998-05-01 | 447 | 0.047284 |
| 1992-06-01 | 445 | 0.047073 |
| 1993-01-01 | 415 | 0.043899 |
| 1993-03-01 | 412 | 0.043582 |
| 1993-07-01 | 404 | 0.042735 |
| 2002-03-01 | 402 | 0.042524 |
| 1997-03-01 | 398 | 0.042101 |
| 1996-07-01 | 379 | 0.040091 |
| 1998-06-01 | 377 | 0.039879 |
| 2004-09-03 | 370 | 0.039139 |
| 1994-08-01 | 369 | 0.039033 |
| 2000-06-01 | 366 | 0.038716 |
| 1993-02-01 | 354 | 0.037446 |
| 1999-04-01 | 343 | 0.036283 |
| 2020-11-04 | 340 | 0.035966 |
| 1994-05-01 | 332 | 0.035119 |
| 2002-09-01 | 332 | 0.035119 |
| 1994-12-01 | 329 | 0.034802 |
| 2003-09-01 | 329 | 0.034802 |
| 1993-12-01 | 325 | 0.034379 |
| 2003-02-01 | 323 | 0.034167 |
| 2000-07-01 | 320 | 0.033850 |
| 1997-11-01 | 312 | 0.033004 |
| 1995-05-01 | 297 | 0.031417 |
| 2003-07-01 | 287 | 0.030359 |
| 2002-10-01 | 279 | 0.029513 |
| 1999-08-01 | 278 | 0.029407 |
| 1995-12-01 | 269 | 0.028455 |
| 2001-11-01 | 263 | 0.027820 |
| 1995-11-01 | 259 | 0.027397 |
| 1995-09-01 | 246 | 0.026022 |
| 1998-08-01 | 245 | 0.025916 |
| 1994-11-01 | 240 | 0.025387 |
| 2001-05-01 | 237 | 0.025070 |
| 2020-10-02 | 233 | 0.024647 |
| 1997-12-01 | 228 | 0.024118 |
| 1992-08-01 | 226 | 0.023906 |
| 1998-07-01 | 224 | 0.023695 |
| 1996-05-01 | 223 | 0.023589 |
| 1994-01-11 | 219 | 0.023166 |
| 1997-06-01 | 216 | 0.022849 |
| 1996-08-01 | 198 | 0.020945 |
| 2000-04-05 | 197 | 0.020839 |
| 1997-05-01 | 196 | 0.020733 |
| 2001-09-01 | 196 | 0.020733 |
| 1993-06-01 | 195 | 0.020627 |
| 1996-09-01 | 195 | 0.020627 |
| 2001-10-01 | 192 | 0.020310 |
| 1993-10-01 | 189 | 0.019993 |
| 1992-07-01 | 185 | 0.019569 |
| 1993-05-01 | 184 | 0.019464 |
| 2000-12-01 | 179 | 0.018935 |
| 1997-04-01 | 174 | 0.018406 |
| 1997-07-01 | 166 | 0.017560 |
| 2000-08-01 | 157 | 0.016608 |
| 1996-11-01 | 151 | 0.015973 |
| 1999-09-01 | 150 | 0.015867 |
| 1999-11-01 | 150 | 0.015867 |
| 1999-10-01 | 147 | 0.015550 |
| 2017-07-01 | 144 | 0.015232 |
| 2000-03-09 | 141 | 0.014915 |
| 2015-01-01 | 140 | 0.014809 |
| 2017-01-01 | 140 | 0.014809 |
| 2014-12-01 | 139 | 0.014704 |
| 1993-04-01 | 139 | 0.014704 |
| 1995-08-01 | 138 | 0.014598 |
| 2015-02-01 | 137 | 0.014492 |
| 2002-06-17 | 134 | 0.014175 |
| 2001-06-01 | 129 | 0.013646 |
| 2000-11-01 | 128 | 0.013540 |
| 1996-10-01 | 126 | 0.013328 |
| 2015-12-01 | 126 | 0.013328 |
| 2015-06-01 | 124 | 0.013117 |
| 2000-09-01 | 123 | 0.013011 |
| 1991-01-20 | 120 | 0.012694 |
| 1992-09-01 | 120 | 0.012694 |
| 2014-01-01 | 118 | 0.012482 |
| 2017-05-01 | 117 | 0.012376 |
| 1997-08-01 | 117 | 0.012376 |
| 1992-10-01 | 115 | 0.012165 |
| 2015-05-01 | 115 | 0.012165 |
| 2013-12-01 | 115 | 0.012165 |
| 2015-03-01 | 115 | 0.012165 |
| 2000-10-01 | 114 | 0.012059 |
| 2002-06-12 | 112 | 0.011847 |
| 1993-08-01 | 112 | 0.011847 |
| 2016-01-01 | 108 | 0.011424 |
| 2016-05-01 | 107 | 0.011319 |
| 2021-02-05 | 106 | 0.011213 |
| 1998-10-01 | 106 | 0.011213 |
| 1997-09-01 | 103 | 0.010895 |
| 2002-06-13 | 102 | 0.010790 |
| 2015-08-01 | 101 | 0.010684 |
| 2014-07-01 | 98 | 0.010367 |
| 2014-05-01 | 97 | 0.010261 |
| 2015-11-01 | 97 | 0.010261 |
| 2014-09-01 | 94 | 0.009943 |
| 2017-02-01 | 91 | 0.009626 |
| 2017-04-01 | 91 | 0.009626 |
| 2018-01-01 | 88 | 0.009309 |
| 2010-12-01 | 88 | 0.009309 |
| 2016-11-01 | 87 | 0.009203 |
| 2018-07-01 | 87 | 0.009203 |
| 2017-03-01 | 87 | 0.009203 |
| 2011-05-01 | 86 | 0.009097 |
| 2014-06-01 | 85 | 0.008991 |
| 2014-11-01 | 84 | 0.008886 |
| 2009-02-01 | 82 | 0.008674 |
| 2020-03-01 | 82 | 0.008674 |
| 2014-03-01 | 81 | 0.008568 |
| 2012-07-01 | 81 | 0.008568 |
| 2013-09-01 | 81 | 0.008568 |
| 2018-12-01 | 80 | 0.008462 |
| 2013-08-01 | 80 | 0.008462 |
| 1993-09-01 | 78 | 0.008251 |
| 2018-04-01 | 77 | 0.008145 |
| 2014-04-01 | 77 | 0.008145 |
| 2012-04-01 | 76 | 0.008039 |
| 2002-06-07 | 76 | 0.008039 |
| 2011-01-01 | 74 | 0.007828 |
| 2014-10-01 | 74 | 0.007828 |
| 2010-09-01 | 74 | 0.007828 |
| 1997-10-01 | 73 | 0.007722 |
| 2017-12-01 | 73 | 0.007722 |
| 2010-05-01 | 71 | 0.007510 |
| 2012-12-01 | 71 | 0.007510 |
| 1991-02-20 | 71 | 0.007510 |
| 2017-10-01 | 70 | 0.007405 |
| 2012-05-01 | 70 | 0.007405 |
| 2002-06-14 | 69 | 0.007299 |
| 2002-06-11 | 68 | 0.007193 |
| 2013-11-01 | 68 | 0.007193 |
| 2013-10-01 | 67 | 0.007087 |
| 2013-06-01 | 67 | 0.007087 |
| 2017-11-01 | 66 | 0.006982 |
| 2016-10-01 | 65 | 0.006876 |
| 2018-09-01 | 65 | 0.006876 |
| 2002-06-06 | 65 | 0.006876 |
| 2002-07-05 | 64 | 0.006770 |
| 2008-02-01 | 64 | 0.006770 |
| 2013-01-01 | 63 | 0.006664 |
| 2011-09-01 | 63 | 0.006664 |
| 2014-02-01 | 63 | 0.006664 |
| 2012-01-01 | 62 | 0.006558 |
| 2018-05-01 | 61 | 0.006453 |
| 2012-09-01 | 60 | 0.006347 |
| 2019-12-01 | 60 | 0.006347 |
| 2020-02-01 | 59 | 0.006241 |
| 2012-06-01 | 58 | 0.006135 |
| 2011-08-01 | 58 | 0.006135 |
| 2013-05-01 | 57 | 0.006030 |
| 2010-01-01 | 56 | 0.005924 |
| 2008-03-01 | 56 | 0.005924 |
| 2002-06-19 | 56 | 0.005924 |
| 1991-12-01 | 54 | 0.005712 |
| 2019-06-01 | 54 | 0.005712 |
| 2010-11-01 | 53 | 0.005606 |
| 2013-04-01 | 53 | 0.005606 |
| 2007-02-01 | 53 | 0.005606 |
| 2002-06-10 | 52 | 0.005501 |
| 2010-08-01 | 52 | 0.005501 |
| 1992-01-02 | 51 | 0.005395 |
| 2018-11-01 | 51 | 0.005395 |
| 2019-11-01 | 51 | 0.005395 |
| 2019-01-01 | 51 | 0.005395 |
| 2008-04-01 | 50 | 0.005289 |
| 2019-09-01 | 50 | 0.005289 |
| 2008-06-01 | 50 | 0.005289 |
| 2009-03-01 | 49 | 0.005183 |
| 2020-04-01 | 49 | 0.005183 |
| 2011-11-01 | 47 | 0.004972 |
| 1991-08-01 | 46 | 0.004866 |
| 2019-05-01 | 46 | 0.004866 |
| 2020-01-01 | 45 | 0.004760 |
| 1991-01-21 | 43 | 0.004549 |
| 1991-11-15 | 42 | 0.004443 |
| 2012-11-01 | 42 | 0.004443 |
| 1991-07-01 | 42 | 0.004443 |
| 2009-09-01 | 41 | 0.004337 |
| 2008-10-01 | 40 | 0.004231 |
| 2008-07-01 | 40 | 0.004231 |
| 2009-01-01 | 40 | 0.004231 |
| 1991-11-06 | 40 | 0.004231 |
| 2010-06-01 | 39 | 0.004125 |
| 2009-08-01 | 39 | 0.004125 |
| 1991-11-01 | 39 | 0.004125 |
| 2009-04-01 | 38 | 0.004020 |
| 2018-10-01 | 38 | 0.004020 |
| 2007-03-01 | 38 | 0.004020 |
| 2005-09-01 | 37 | 0.003914 |
| 1991-11-11 | 36 | 0.003808 |
| 2009-10-01 | 35 | 0.003702 |
| 2011-10-01 | 35 | 0.003702 |
| 2008-01-01 | 34 | 0.003597 |
| 2009-06-01 | 33 | 0.003491 |
| 2009-11-01 | 33 | 0.003491 |
| 2007-01-01 | 33 | 0.003491 |
| 2006-05-01 | 33 | 0.003491 |
| 2002-06-18 | 33 | 0.003491 |
| 2006-04-01 | 32 | 0.003385 |
| 1992-03-02 | 32 | 0.003385 |
| 2020-05-01 | 32 | 0.003385 |
| 2002-07-04 | 32 | 0.003385 |
| 2009-12-01 | 32 | 0.003385 |
| 2005-12-01 | 32 | 0.003385 |
| 2005-08-01 | 31 | 0.003279 |
| 2006-12-01 | 30 | 0.003173 |
| 2002-06-28 | 30 | 0.003173 |
| 2007-10-01 | 30 | 0.003173 |
| 2007-06-01 | 29 | 0.003068 |
| 2008-05-01 | 29 | 0.003068 |
| 2008-08-01 | 29 | 0.003068 |
| 2007-04-01 | 29 | 0.003068 |
| 2002-06-21 | 28 | 0.002962 |
| 1992-06-02 | 28 | 0.002962 |
| 2009-05-01 | 27 | 0.002856 |
| 1992-02-02 | 26 | 0.002750 |
| 2006-07-01 | 26 | 0.002750 |
| 1991-06-03 | 25 | 0.002645 |
| 1992-01-14 | 25 | 0.002645 |
| 1995-01-02 | 25 | 0.002645 |
| 2007-12-01 | 25 | 0.002645 |
| 1994-10-06 | 25 | 0.002645 |
| 2010-10-01 | 24 | 0.002539 |
| 1991-10-01 | 24 | 0.002539 |
| 2005-10-01 | 24 | 0.002539 |
| 1994-03-28 | 23 | 0.002433 |
| 2007-11-01 | 23 | 0.002433 |
| 2009-07-01 | 23 | 0.002433 |
| 2006-08-01 | 23 | 0.002433 |
| 2007-08-01 | 23 | 0.002433 |
| 1991-03-25 | 22 | 0.002327 |
| 2019-10-01 | 22 | 0.002327 |
| 1995-10-02 | 22 | 0.002327 |
| 2006-06-01 | 22 | 0.002327 |
| 2006-10-01 | 20 | 0.002116 |
| 2005-11-01 | 20 | 0.002116 |
| 2006-09-01 | 19 | 0.002010 |
| 2007-09-01 | 19 | 0.002010 |
| 2007-05-01 | 19 | 0.002010 |
| 1993-04-05 | 16 | 0.001692 |
| 2008-11-01 | 15 | 0.001587 |
| 1992-02-01 | 14 | 0.001481 |
| 1991-09-01 | 13 | 0.001375 |
| 1993-11-08 | 13 | 0.001375 |
| 1994-07-04 | 13 | 0.001375 |
| 2007-07-01 | 13 | 0.001375 |
| 1993-10-04 | 13 | 0.001375 |
| 1996-01-02 | 12 | 0.001269 |
| 1994-03-04 | 12 | 0.001269 |
| 1992-04-01 | 12 | 0.001269 |
| 2006-11-01 | 12 | 0.001269 |
| 1993-12-07 | 11 | 0.001164 |
| 1994-01-07 | 11 | 0.001164 |
| 2002-07-03 | 10 | 0.001058 |
| 2006-01-01 | 10 | 0.001058 |
| 1992-12-14 | 10 | 0.001058 |
| 1992-09-30 | 10 | 0.001058 |
| 1993-01-07 | 9 | 0.000952 |
| 1993-09-16 | 8 | 0.000846 |
| 1993-03-11 | 8 | 0.000846 |
| 1991-05-16 | 7 | 0.000740 |
| 2020-06-01 | 7 | 0.000740 |
| 2002-07-01 | 6 | 0.000635 |
| 2021-12-01 | 6 | 0.000635 |
| 1992-01-16 | 6 | 0.000635 |
| 1995-09-07 | 5 | 0.000529 |
| 1994-02-07 | 5 | 0.000529 |
| 1996-09-02 | 5 | 0.000529 |
| 1994-06-06 | 5 | 0.000529 |
| 2021-06-01 | 5 | 0.000529 |
| 1993-02-12 | 5 | 0.000529 |
| 2002-06-05 | 5 | 0.000529 |
| 1993-08-05 | 5 | 0.000529 |
| 1991-01-10 | 4 | 0.000423 |
| 1990-12-10 | 4 | 0.000423 |
| 2020-08-01 | 4 | 0.000423 |
| 1995-05-02 | 4 | 0.000423 |
| 2021-07-01 | 4 | 0.000423 |
| 2021-11-02 | 4 | 0.000423 |
| 2021-04-01 | 3 | 0.000317 |
| 2002-06-27 | 3 | 0.000317 |
| 2002-06-25 | 3 | 0.000317 |
| 2002-06-26 | 3 | 0.000317 |
| 1991-04-02 | 3 | 0.000317 |
| 1990-10-01 | 3 | 0.000317 |
| 2022-11-02 | 3 | 0.000317 |
| 1991-01-01 | 3 | 0.000317 |
| 2020-10-05 | 3 | 0.000317 |
| 1991-05-08 | 3 | 0.000317 |
| 1992-09-25 | 3 | 0.000317 |
| 2002-07-02 | 3 | 0.000317 |
| 1992-10-14 | 3 | 0.000317 |
| 1996-08-02 | 3 | 0.000317 |
| 1993-04-12 | 3 | 0.000317 |
| 1996-09-30 | 2 | 0.000212 |
| 1993-02-17 | 2 | 0.000212 |
| 1994-12-04 | 2 | 0.000212 |
| 1995-10-21 | 2 | 0.000212 |
| 1997-05-04 | 2 | 0.000212 |
| 2002-05-28 | 2 | 0.000212 |
| 1999-12-28 | 2 | 0.000212 |
| 1998-08-18 | 2 | 0.000212 |
| 2002-06-01 | 2 | 0.000212 |
| 2021-08-02 | 2 | 0.000212 |
| 1990-03-01 | 2 | 0.000212 |
| 1990-02-20 | 2 | 0.000212 |
| 1990-09-25 | 2 | 0.000212 |
| 1998-08-21 | 2 | 0.000212 |
| 1993-02-09 | 2 | 0.000212 |
| 1994-08-19 | 2 | 0.000212 |
| 1998-12-18 | 2 | 0.000212 |
| 1991-10-25 | 2 | 0.000212 |
| 1996-12-21 | 2 | 0.000212 |
| 2022-01-03 | 2 | 0.000212 |
| 2002-06-03 | 2 | 0.000212 |
| 1993-07-05 | 2 | 0.000212 |
| 2002-07-19 | 1 | 0.000106 |
| 2002-07-12 | 1 | 0.000106 |
| 2002-07-28 | 1 | 0.000106 |
| 2002-06-04 | 1 | 0.000106 |
| 2002-07-10 | 1 | 0.000106 |
| 1992-09-23 | 1 | 0.000106 |
| 1992-05-18 | 1 | 0.000106 |
| 2023-06-01 | 1 | 0.000106 |
| 1996-09-05 | 1 | 0.000106 |
| 1991-03-15 | 1 | 0.000106 |
| 1996-06-03 | 1 | 0.000106 |
| 2021-02-01 | 1 | 0.000106 |
| 1991-10-21 | 1 | 0.000106 |
| 1990-01-01 | 1 | 0.000106 |
| 1994-10-10 | 1 | 0.000106 |
| 1992-09-19 | 1 | 0.000106 |
| 1996-07-12 | 1 | 0.000106 |
| 1994-07-28 | 1 | 0.000106 |
| 1999-04-22 | 1 | 0.000106 |
| 2021-05-03 | 1 | 0.000106 |
| 1996-03-20 | 1 | 0.000106 |
| 1996-12-26 | 1 | 0.000106 |
| 2000-02-14 | 1 | 0.000106 |
| 2000-03-30 | 1 | 0.000106 |
| 1991-06-01 | 1 | 0.000106 |
| 2014-04-05 | 1 | 0.000106 |
| 2000-04-26 | 1 | 0.000106 |
| 1990-01-10 | 1 | 0.000106 |
| 1991-01-08 | 1 | 0.000106 |
| 1994-01-10 | 1 | 0.000106 |
| 1991-04-01 | 1 | 0.000106 |
| 1996-12-28 | 1 | 0.000106 |
| 1997-06-04 | 1 | 0.000106 |
| 1992-08-14 | 1 | 0.000106 |
| 1992-05-15 | 1 | 0.000106 |
| 1990-04-20 | 1 | 0.000106 |
| 1994-02-14 | 1 | 0.000106 |
| 1991-09-03 | 1 | 0.000106 |
| 1991-01-31 | 1 | 0.000106 |
| 1992-03-24 | 1 | 0.000106 |
| 1995-07-19 | 1 | 0.000106 |
| 1992-06-10 | 1 | 0.000106 |
| 1992-04-02 | 1 | 0.000106 |
| 1990-11-01 | 1 | 0.000106 |
| 1996-09-25 | 1 | 0.000106 |
| 2002-07-17 | 1 | 0.000106 |
| 2002-07-27 | 1 | 0.000106 |
| 1993-04-03 | 1 | 0.000106 |
| 1994-10-11 | 1 | 0.000106 |
| 1990-01-20 | 1 | 0.000106 |
| 1993-02-15 | 1 | 0.000106 |
| 1999-03-25 | 1 | 0.000106 |
| 1993-05-04 | 1 | 0.000106 |
| 1993-02-05 | 1 | 0.000106 |
| 1994-09-10 | 1 | 0.000106 |
| 1993-02-04 | 1 | 0.000106 |
| 1993-02-08 | 1 | 0.000106 |
| 1997-04-02 | 1 | 0.000106 |
| 1999-08-12 | 1 | 0.000106 |
| 2000-03-28 | 1 | 0.000106 |
| 1990-06-10 | 1 | 0.000106 |
| 1991-04-23 | 1 | 0.000106 |
| 1998-10-20 | 1 | 0.000106 |
| 1992-03-01 | 1 | 0.000106 |
| 1993-04-19 | 1 | 0.000106 |
| 1993-03-10 | 1 | 0.000106 |
| 1991-05-05 | 1 | 0.000106 |
| 1998-02-05 | 1 | 0.000106 |
| 1999-10-25 | 1 | 0.000106 |
| 1991-04-04 | 1 | 0.000106 |
| 1998-02-09 | 1 | 0.000106 |
| 2022-06-01 | 1 | 0.000106 |
| 1993-04-30 | 1 | 0.000106 |
| 1999-10-27 | 1 | 0.000106 |
| 1995-10-11 | 1 | 0.000106 |
| 1990-03-31 | 1 | 0.000106 |
| 1992-05-21 | 1 | 0.000106 |
| 1995-10-26 | 1 | 0.000106 |
| 1996-11-09 | 1 | 0.000106 |
| 1992-09-29 | 1 | 0.000106 |
| 1991-08-03 | 1 | 0.000106 |
| 1992-11-02 | 1 | 0.000106 |
| 1991-03-20 | 1 | 0.000106 |
| 2022-03-09 | 1 | 0.000106 |
| 2022-05-02 | 1 | 0.000106 |
| 2023-04-03 | 1 | 0.000106 |
| 2022-07-04 | 1 | 0.000106 |
| 1992-05-22 | 1 | 0.000106 |
| 1993-03-08 | 1 | 0.000106 |
| 1991-06-20 | 1 | 0.000106 |
| 2020-12-01 | 1 | 0.000106 |
| 1991-01-04 | 1 | 0.000106 |
| 1990-02-12 | 1 | 0.000106 |
| 1991-12-03 | 1 | 0.000106 |
| 1996-12-20 | 1 | 0.000106 |
| 1992-06-08 | 1 | 0.000106 |
| 1996-12-13 | 1 | 0.000106 |
| 1992-11-19 | 1 | 0.000106 |
| 1993-03-16 | 1 | 0.000106 |
| 1992-02-03 | 1 | 0.000106 |
| 1996-01-30 | 1 | 0.000106 |
# Vamos a realizar analisis por cada variable
var = "msf_datelastrecurringdonorquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastrecurringdonorquota__c es 858069. Lo que supone un 47.58012419742722% El nº de vacios para la variable msf_datelastrecurringdonorquota__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2023-07-04 | 393458 | 41.620352 |
| 2023-06-02 | 22387 | 2.368118 |
| 2023-05-03 | 21046 | 2.226265 |
| 2023-01-03 | 14510 | 1.534881 |
| 2023-02-02 | 13748 | 1.454276 |
| 2023-03-02 | 11212 | 1.186016 |
| 2022-12-02 | 10506 | 1.111334 |
| 2023-04-04 | 9691 | 1.025123 |
| 2022-11-03 | 8475 | 0.896493 |
| 2022-10-04 | 7566 | 0.800338 |
| 2022-08-02 | 7167 | 0.758132 |
| 2020-09-01 | 6699 | 0.708626 |
| 2022-09-02 | 6368 | 0.673613 |
| 2018-02-01 | 5264 | 0.556831 |
| 2018-01-03 | 4175 | 0.441635 |
| 2021-04-02 | 3879 | 0.410324 |
| 2022-01-04 | 3867 | 0.409055 |
| 2017-12-04 | 3853 | 0.407574 |
| 2019-01-02 | 3847 | 0.406939 |
| 2018-12-03 | 3757 | 0.397419 |
| 2021-01-05 | 3666 | 0.387793 |
| 2019-12-02 | 3651 | 0.386206 |
| 2021-12-02 | 3630 | 0.383985 |
| 2021-07-02 | 3591 | 0.379859 |
| 2021-10-02 | 3581 | 0.378802 |
| 2018-03-01 | 3569 | 0.377532 |
| 2021-11-03 | 3438 | 0.363675 |
| 2021-06-02 | 3421 | 0.361877 |
| 2022-07-05 | 3410 | 0.360713 |
| 2022-05-03 | 3400 | 0.359655 |
| 2021-03-02 | 3396 | 0.359232 |
| 2021-05-04 | 3395 | 0.359126 |
| 2018-09-03 | 3323 | 0.351510 |
| 2022-02-02 | 3290 | 0.348019 |
| 2021-02-02 | 3277 | 0.346644 |
| 2018-10-02 | 3275 | 0.346433 |
| 2020-01-02 | 3258 | 0.344634 |
| 2022-04-02 | 3253 | 0.344105 |
| 2018-04-03 | 3230 | 0.341672 |
| 2018-07-02 | 3223 | 0.340932 |
| 2022-06-02 | 3201 | 0.338605 |
| 2019-02-01 | 3144 | 0.332575 |
| 2021-08-03 | 3143 | 0.332469 |
| 2020-02-03 | 3126 | 0.330671 |
| 2017-10-02 | 3114 | 0.329402 |
| 2019-10-02 | 3114 | 0.329402 |
| 2018-08-01 | 3107 | 0.328661 |
| 2020-03-02 | 3099 | 0.327815 |
| 2019-08-01 | 3098 | 0.327709 |
| 2022-03-02 | 3095 | 0.327392 |
| 2018-06-01 | 3075 | 0.325276 |
| 2019-09-02 | 3063 | 0.324007 |
| 2017-11-02 | 3032 | 0.320728 |
| 2018-11-02 | 3027 | 0.320199 |
| 2019-07-01 | 3024 | 0.319882 |
| 2019-03-01 | 2966 | 0.313746 |
| 2017-01-02 | 2947 | 0.311736 |
| 2016-12-01 | 2909 | 0.307717 |
| 2018-05-03 | 2882 | 0.304861 |
| 2017-09-01 | 2868 | 0.303380 |
| 2019-04-01 | 2811 | 0.297350 |
| 2019-11-04 | 2788 | 0.294917 |
| 2021-09-02 | 2681 | 0.283599 |
| 2019-05-02 | 2640 | 0.279262 |
| 2017-08-01 | 2596 | 0.274607 |
| 2019-06-03 | 2538 | 0.268472 |
| 2017-07-03 | 2517 | 0.266251 |
| 2015-12-02 | 2422 | 0.256201 |
| 2016-10-03 | 2399 | 0.253768 |
| 2017-02-02 | 2394 | 0.253240 |
| 2017-03-02 | 2383 | 0.252076 |
| 2017-05-02 | 2360 | 0.249643 |
| 2020-04-02 | 2339 | 0.247422 |
| 2017-06-01 | 2336 | 0.247104 |
| 2016-02-01 | 2269 | 0.240017 |
| 2017-04-03 | 2269 | 0.240017 |
| 2016-11-02 | 2265 | 0.239594 |
| 2012-12-03 | 2252 | 0.238219 |
| 2016-01-04 | 2217 | 0.234516 |
| 2016-09-01 | 2209 | 0.233670 |
| 2020-07-01 | 2190 | 0.231660 |
| 2015-01-02 | 2138 | 0.226160 |
| 2012-01-02 | 2137 | 0.226054 |
| 2014-01-02 | 2131 | 0.225419 |
| 2014-12-02 | 2112 | 0.223409 |
| 2020-05-03 | 2102 | 0.222352 |
| 2016-08-01 | 2088 | 0.220871 |
| 2015-10-01 | 2083 | 0.220342 |
| 2016-07-01 | 2081 | 0.220130 |
| 2020-06-02 | 2066 | 0.218543 |
| 2013-01-02 | 2053 | 0.217168 |
| 2016-03-01 | 2029 | 0.214630 |
| 2012-10-01 | 2027 | 0.214418 |
| 2016-04-01 | 2026 | 0.214312 |
| 2012-02-01 | 2020 | 0.213677 |
| 2015-09-01 | 2014 | 0.213043 |
| 2011-12-01 | 2009 | 0.212514 |
| 2016-06-01 | 2003 | 0.211879 |
| 2013-02-01 | 1981 | 0.209552 |
| 2015-02-02 | 1980 | 0.209446 |
| 2015-03-02 | 1975 | 0.208917 |
| 2015-11-03 | 1971 | 0.208494 |
| 2015-04-01 | 1963 | 0.207648 |
| 2014-02-03 | 1962 | 0.207542 |
| 2016-05-02 | 1924 | 0.203523 |
| 2013-12-02 | 1909 | 0.201936 |
| 2015-07-01 | 1898 | 0.200772 |
| 2015-06-02 | 1880 | 0.198868 |
| 2013-03-01 | 1831 | 0.193685 |
| 2015-08-03 | 1819 | 0.192416 |
| 2012-03-01 | 1807 | 0.191146 |
| 2012-04-02 | 1805 | 0.190935 |
| 2013-10-02 | 1787 | 0.189031 |
| 2012-05-03 | 1787 | 0.189031 |
| 2012-11-02 | 1780 | 0.188290 |
| 2014-03-03 | 1775 | 0.187761 |
| 2020-11-04 | 1766 | 0.186809 |
| 2014-10-02 | 1761 | 0.186280 |
| 2012-09-03 | 1761 | 0.186280 |
| 2012-07-02 | 1753 | 0.185434 |
| 2015-05-04 | 1730 | 0.183001 |
| 2013-04-02 | 1709 | 0.180780 |
| 2013-09-02 | 1666 | 0.176231 |
| 2014-09-03 | 1655 | 0.175067 |
| 2014-04-02 | 1632 | 0.172634 |
| 2012-08-01 | 1632 | 0.172634 |
| 2014-05-05 | 1627 | 0.172106 |
| 2014-08-01 | 1623 | 0.171682 |
| 2014-07-02 | 1601 | 0.169355 |
| 2014-11-03 | 1599 | 0.169144 |
| 2020-10-02 | 1593 | 0.168509 |
| 2013-07-01 | 1581 | 0.167240 |
| 2010-12-02 | 1537 | 0.162585 |
| 2011-01-03 | 1536 | 0.162480 |
| 2011-03-01 | 1530 | 0.161845 |
| 2013-05-02 | 1519 | 0.160681 |
| 2012-06-04 | 1518 | 0.160575 |
| 2011-11-02 | 1480 | 0.156556 |
| 2014-06-05 | 1471 | 0.155604 |
| 2020-12-02 | 1437 | 0.152007 |
| 2011-10-04 | 1432 | 0.151478 |
| 2011-09-02 | 1428 | 0.151055 |
| 2011-02-01 | 1422 | 0.150420 |
| 2013-11-04 | 1418 | 0.149997 |
| 2011-07-01 | 1386 | 0.146612 |
| 2011-06-01 | 1368 | 0.144708 |
| 2013-08-02 | 1362 | 0.144074 |
| 2011-04-01 | 1350 | 0.142804 |
| 2013-06-03 | 1342 | 0.141958 |
| 2011-05-02 | 1273 | 0.134659 |
| 2009-02-03 | 1268 | 0.134130 |
| 2009-01-02 | 1245 | 0.131697 |
| 2011-08-02 | 1238 | 0.130957 |
| 2010-10-04 | 1215 | 0.128524 |
| 2008-12-01 | 1205 | 0.127466 |
| 2008-10-02 | 1187 | 0.125562 |
| 2010-09-02 | 1180 | 0.124821 |
| 2009-12-02 | 1179 | 0.124716 |
| 2010-11-02 | 1173 | 0.124081 |
| 2008-09-01 | 1155 | 0.122177 |
| 2010-08-02 | 1097 | 0.116042 |
| 2010-07-01 | 1080 | 0.114243 |
| 2008-01-03 | 1066 | 0.112762 |
| 2010-05-03 | 1066 | 0.112762 |
| 2008-03-03 | 1042 | 0.110224 |
| 2010-06-02 | 1041 | 0.110118 |
| 2020-08-03 | 1034 | 0.109377 |
| 2010-04-01 | 1034 | 0.109377 |
| 2009-03-03 | 1031 | 0.109060 |
| 2010-02-01 | 1026 | 0.108531 |
| 2008-02-04 | 1026 | 0.108531 |
| 2010-03-01 | 1024 | 0.108320 |
| 2009-04-02 | 1023 | 0.108214 |
| 2010-01-04 | 1022 | 0.108108 |
| 2009-10-02 | 1021 | 0.108002 |
| 2007-12-02 | 982 | 0.103877 |
| 2009-11-02 | 967 | 0.102290 |
| 2008-11-03 | 945 | 0.099963 |
| 2008-07-04 | 945 | 0.099963 |
| 2009-09-02 | 938 | 0.099223 |
| 2008-05-02 | 926 | 0.097953 |
| 2009-05-04 | 895 | 0.094674 |
| 2008-04-04 | 894 | 0.094568 |
| 2008-06-02 | 866 | 0.091606 |
| 2007-04-02 | 863 | 0.091289 |
| 2009-06-04 | 846 | 0.089491 |
| 2009-07-02 | 842 | 0.089068 |
| 2007-10-04 | 840 | 0.088856 |
| 2007-11-02 | 836 | 0.088433 |
| 2007-07-04 | 806 | 0.085259 |
| 2007-09-03 | 804 | 0.085048 |
| 2007-01-04 | 772 | 0.081663 |
| 2007-03-02 | 768 | 0.081240 |
| 2009-08-03 | 760 | 0.080394 |
| 2008-08-08 | 749 | 0.079230 |
| 2007-02-05 | 716 | 0.075739 |
| 2007-05-04 | 702 | 0.074258 |
| 2007-08-02 | 698 | 0.073835 |
| 2007-06-05 | 646 | 0.068334 |
| 2002-05-01 | 643 | 0.068017 |
| 2006-10-02 | 627 | 0.066325 |
| 2006-02-03 | 601 | 0.063574 |
| 2006-01-05 | 597 | 0.063151 |
| 2006-12-02 | 571 | 0.060401 |
| 2006-09-04 | 541 | 0.057227 |
| 2005-12-03 | 532 | 0.056275 |
| 2006-06-02 | 521 | 0.055112 |
| 2006-03-03 | 521 | 0.055112 |
| 2006-04-03 | 520 | 0.055006 |
| 2006-07-03 | 518 | 0.054795 |
| 2006-11-03 | 518 | 0.054795 |
| 2006-05-04 | 507 | 0.053631 |
| 2006-08-02 | 494 | 0.052256 |
| 2005-10-03 | 448 | 0.047390 |
| 2005-04-04 | 441 | 0.046649 |
| 2005-09-02 | 439 | 0.046438 |
| 2005-03-04 | 428 | 0.045274 |
| 2005-08-02 | 423 | 0.044745 |
| 2005-02-04 | 418 | 0.044216 |
| 2005-11-03 | 406 | 0.042947 |
| 2005-05-04 | 396 | 0.041889 |
| 2005-01-04 | 390 | 0.041255 |
| 2005-06-03 | 385 | 0.040726 |
| 2004-09-03 | 380 | 0.040197 |
| 2005-07-04 | 357 | 0.037764 |
| 2004-02-01 | 355 | 0.037552 |
| 2004-03-01 | 328 | 0.034696 |
| 2004-01-01 | 322 | 0.034061 |
| 2004-07-01 | 321 | 0.033956 |
| 2004-10-06 | 315 | 0.033321 |
| 2004-04-01 | 307 | 0.032475 |
| 2003-01-01 | 286 | 0.030253 |
| 2004-11-04 | 286 | 0.030253 |
| 2003-02-01 | 277 | 0.029301 |
| 2004-12-05 | 273 | 0.028878 |
| 2000-10-01 | 267 | 0.028244 |
| 2004-05-01 | 263 | 0.027820 |
| 2002-10-01 | 254 | 0.026868 |
| 2001-10-01 | 250 | 0.026445 |
| 2001-02-01 | 242 | 0.025599 |
| 2003-04-01 | 236 | 0.024964 |
| 2004-06-01 | 236 | 0.024964 |
| 2003-12-01 | 235 | 0.024859 |
| 2002-01-01 | 233 | 0.024647 |
| 2003-10-01 | 215 | 0.022743 |
| 2002-09-01 | 207 | 0.021897 |
| 2003-07-01 | 206 | 0.021791 |
| 2003-09-01 | 206 | 0.021791 |
| 2004-08-01 | 205 | 0.021685 |
| 2003-03-01 | 203 | 0.021474 |
| 2001-07-01 | 200 | 0.021156 |
| 2003-11-01 | 199 | 0.021050 |
| 1996-10-01 | 194 | 0.020521 |
| 2001-04-01 | 187 | 0.019781 |
| 2003-06-01 | 187 | 0.019781 |
| 2002-08-01 | 186 | 0.019675 |
| 2001-01-01 | 185 | 0.019569 |
| 2003-05-01 | 182 | 0.019252 |
| 2001-03-01 | 178 | 0.018829 |
| 2003-08-01 | 176 | 0.018617 |
| 2001-12-01 | 171 | 0.018089 |
| 2002-12-01 | 170 | 0.017983 |
| 2002-11-01 | 168 | 0.017771 |
| 2002-02-01 | 165 | 0.017454 |
| 1997-10-01 | 162 | 0.017137 |
| 1997-01-01 | 162 | 0.017137 |
| 2000-12-01 | 157 | 0.016608 |
| 2000-04-05 | 155 | 0.016396 |
| 2000-07-01 | 154 | 0.016290 |
| 1998-02-01 | 154 | 0.016290 |
| 2002-04-01 | 153 | 0.016184 |
| 2001-09-01 | 152 | 0.016079 |
| 2001-11-01 | 151 | 0.015973 |
| 1997-02-01 | 148 | 0.015656 |
| 1995-10-02 | 146 | 0.015444 |
| 1999-02-01 | 146 | 0.015444 |
| 2000-02-01 | 145 | 0.015338 |
| 2002-03-01 | 145 | 0.015338 |
| 1997-04-01 | 143 | 0.015127 |
| 1996-04-01 | 143 | 0.015127 |
| 1999-01-01 | 136 | 0.014386 |
| 1996-07-01 | 133 | 0.014069 |
| 2000-09-01 | 131 | 0.013857 |
| 1996-02-01 | 130 | 0.013752 |
| 2001-06-01 | 130 | 0.013752 |
| 1999-07-01 | 129 | 0.013646 |
| 1997-07-01 | 129 | 0.013646 |
| 2000-05-01 | 124 | 0.013117 |
| 1999-04-01 | 123 | 0.013011 |
| 1998-01-01 | 122 | 0.012905 |
| 2001-08-01 | 121 | 0.012799 |
| 1998-10-01 | 120 | 0.012694 |
| 1998-04-01 | 120 | 0.012694 |
| 2000-08-01 | 118 | 0.012482 |
| 2001-05-01 | 116 | 0.012271 |
| 2000-11-01 | 114 | 0.012059 |
| 2000-06-01 | 109 | 0.011530 |
| 2017-07-01 | 108 | 0.011424 |
| 1998-03-01 | 108 | 0.011424 |
| 1996-01-02 | 107 | 0.011319 |
| 2000-01-13 | 107 | 0.011319 |
| 1995-02-01 | 107 | 0.011319 |
| 1999-10-01 | 107 | 0.011319 |
| 2002-07-10 | 104 | 0.011001 |
| 2000-03-09 | 100 | 0.010578 |
| 1999-12-01 | 99 | 0.010472 |
| 1998-07-01 | 97 | 0.010261 |
| 1995-07-01 | 97 | 0.010261 |
| 1995-04-01 | 91 | 0.009626 |
| 1999-03-01 | 90 | 0.009520 |
| 1998-12-01 | 89 | 0.009415 |
| 1997-03-01 | 85 | 0.008991 |
| 2002-07-13 | 83 | 0.008780 |
| 1995-01-02 | 83 | 0.008780 |
| 1999-06-01 | 82 | 0.008674 |
| 1999-09-01 | 75 | 0.007934 |
| 2017-01-01 | 73 | 0.007722 |
| 1996-03-01 | 73 | 0.007722 |
| 2017-12-01 | 71 | 0.007510 |
| 1999-08-01 | 70 | 0.007405 |
| 1996-12-01 | 68 | 0.007193 |
| 1999-11-01 | 68 | 0.007193 |
| 2015-12-01 | 67 | 0.007087 |
| 2002-06-13 | 66 | 0.006982 |
| 2017-02-01 | 66 | 0.006982 |
| 2018-01-01 | 66 | 0.006982 |
| 2015-02-01 | 65 | 0.006876 |
| 2017-05-01 | 64 | 0.006770 |
| 2017-03-01 | 63 | 0.006664 |
| 2015-05-01 | 63 | 0.006664 |
| 2017-11-01 | 61 | 0.006453 |
| 2016-05-01 | 61 | 0.006453 |
| 2015-08-01 | 61 | 0.006453 |
| 1995-11-01 | 59 | 0.006241 |
| 1997-06-01 | 59 | 0.006241 |
| 2015-06-01 | 59 | 0.006241 |
| 1998-09-01 | 58 | 0.006135 |
| 1995-03-01 | 57 | 0.006030 |
| 2016-10-01 | 57 | 0.006030 |
| 2015-11-01 | 56 | 0.005924 |
| 1998-06-01 | 55 | 0.005818 |
| 2015-03-01 | 55 | 0.005818 |
| 1996-09-02 | 55 | 0.005818 |
| 1996-11-01 | 54 | 0.005712 |
| 2016-01-01 | 53 | 0.005606 |
| 2017-04-01 | 52 | 0.005501 |
| 2015-01-01 | 52 | 0.005501 |
| 1994-10-06 | 52 | 0.005501 |
| 2014-03-01 | 52 | 0.005501 |
| 1995-06-01 | 52 | 0.005501 |
| 1999-05-01 | 52 | 0.005501 |
| 2018-10-01 | 51 | 0.005395 |
| 2014-12-01 | 50 | 0.005289 |
| 2017-10-01 | 50 | 0.005289 |
| 2016-11-01 | 50 | 0.005289 |
| 1998-08-01 | 50 | 0.005289 |
| 2014-09-01 | 50 | 0.005289 |
| 2013-12-01 | 49 | 0.005183 |
| 2018-04-01 | 48 | 0.005077 |
| 2013-09-01 | 48 | 0.005077 |
| 1995-12-01 | 48 | 0.005077 |
| 2018-07-01 | 47 | 0.004972 |
| 1998-11-01 | 47 | 0.004972 |
| 2014-05-01 | 46 | 0.004866 |
| 1997-09-01 | 46 | 0.004866 |
| 2012-05-01 | 45 | 0.004760 |
| 1994-10-01 | 45 | 0.004760 |
| 1997-11-01 | 45 | 0.004760 |
| 2009-02-01 | 45 | 0.004760 |
| 2012-04-01 | 44 | 0.004654 |
| 2018-12-01 | 44 | 0.004654 |
| 1997-08-01 | 43 | 0.004549 |
| 2019-12-01 | 42 | 0.004443 |
| 1996-06-03 | 42 | 0.004443 |
| 2018-11-01 | 41 | 0.004337 |
| 1997-05-01 | 40 | 0.004231 |
| 1997-12-01 | 40 | 0.004231 |
| 1994-07-04 | 39 | 0.004125 |
| 2013-11-01 | 39 | 0.004125 |
| 1996-05-01 | 38 | 0.004020 |
| 2012-07-01 | 38 | 0.004020 |
| 1995-09-07 | 38 | 0.004020 |
| 1998-05-01 | 37 | 0.003914 |
| 2020-02-01 | 37 | 0.003914 |
| 1994-11-01 | 37 | 0.003914 |
| 2014-10-01 | 37 | 0.003914 |
| 1994-02-01 | 36 | 0.003808 |
| 2014-07-01 | 35 | 0.003702 |
| 2012-09-01 | 35 | 0.003702 |
| 2014-11-01 | 35 | 0.003702 |
| 2008-10-01 | 35 | 0.003702 |
| 1996-08-02 | 34 | 0.003597 |
| 1994-12-04 | 34 | 0.003597 |
| 2014-04-01 | 34 | 0.003597 |
| 2013-10-01 | 33 | 0.003491 |
| 2014-06-01 | 33 | 0.003491 |
| 2011-05-01 | 33 | 0.003491 |
| 1994-03-28 | 32 | 0.003385 |
| 2010-12-01 | 31 | 0.003279 |
| 2020-03-01 | 31 | 0.003279 |
| 2019-05-01 | 31 | 0.003279 |
| 2020-01-01 | 31 | 0.003279 |
| 2009-03-01 | 30 | 0.003173 |
| 2019-09-01 | 30 | 0.003173 |
| 1994-01-07 | 30 | 0.003173 |
| 2013-05-01 | 30 | 0.003173 |
| 2014-02-01 | 30 | 0.003173 |
| 2018-05-01 | 29 | 0.003068 |
| 2002-06-05 | 29 | 0.003068 |
| 2013-01-01 | 28 | 0.002962 |
| 2018-09-01 | 28 | 0.002962 |
| 2019-11-01 | 28 | 0.002962 |
| 2012-01-01 | 28 | 0.002962 |
| 1995-05-02 | 27 | 0.002856 |
| 2009-04-01 | 27 | 0.002856 |
| 2019-01-01 | 27 | 0.002856 |
| 2011-01-01 | 27 | 0.002856 |
| 2020-04-01 | 27 | 0.002856 |
| 2012-11-01 | 27 | 0.002856 |
| 1993-07-01 | 27 | 0.002856 |
| 1993-04-05 | 26 | 0.002750 |
| 2009-09-01 | 26 | 0.002750 |
| 2002-06-08 | 26 | 0.002750 |
| 2009-08-01 | 26 | 0.002750 |
| 2009-01-01 | 26 | 0.002750 |
| 2011-11-01 | 25 | 0.002645 |
| 2012-12-01 | 25 | 0.002645 |
| 2010-05-01 | 25 | 0.002645 |
| 2011-10-01 | 25 | 0.002645 |
| 2011-08-01 | 25 | 0.002645 |
| 1993-10-04 | 24 | 0.002539 |
| 2013-04-01 | 24 | 0.002539 |
| 2008-06-01 | 24 | 0.002539 |
| 2008-04-01 | 24 | 0.002539 |
| 2019-10-01 | 23 | 0.002433 |
| 2013-06-01 | 23 | 0.002433 |
| 2008-02-01 | 23 | 0.002433 |
| 2020-06-01 | 22 | 0.002327 |
| 2020-05-01 | 22 | 0.002327 |
| 1993-07-05 | 22 | 0.002327 |
| 1995-08-01 | 22 | 0.002327 |
| 2010-11-01 | 21 | 0.002221 |
| 2019-06-01 | 20 | 0.002116 |
| 2008-07-01 | 20 | 0.002116 |
| 2009-10-01 | 20 | 0.002116 |
| 2014-01-01 | 20 | 0.002116 |
| 2011-09-01 | 20 | 0.002116 |
| 2008-11-01 | 20 | 0.002116 |
| 2012-06-01 | 20 | 0.002116 |
| 2008-03-01 | 19 | 0.002010 |
| 2007-11-01 | 19 | 0.002010 |
| 2013-08-01 | 19 | 0.002010 |
| 2005-12-01 | 18 | 0.001904 |
| 2010-06-01 | 18 | 0.001904 |
| 1992-06-01 | 18 | 0.001904 |
| 2009-06-01 | 18 | 0.001904 |
| 1995-10-01 | 18 | 0.001904 |
| 2010-01-01 | 17 | 0.001798 |
| 2010-09-01 | 17 | 0.001798 |
| 2021-02-05 | 17 | 0.001798 |
| 2009-05-01 | 17 | 0.001798 |
| 2007-08-01 | 17 | 0.001798 |
| 1994-02-07 | 17 | 0.001798 |
| 2008-08-01 | 17 | 0.001798 |
| 2010-08-01 | 17 | 0.001798 |
| 1992-11-01 | 17 | 0.001798 |
| 2006-04-01 | 16 | 0.001692 |
| 1993-03-01 | 16 | 0.001692 |
| 2009-12-01 | 16 | 0.001692 |
| 1993-11-08 | 16 | 0.001692 |
| 1994-09-01 | 16 | 0.001692 |
| 1994-07-01 | 15 | 0.001587 |
| 1994-03-04 | 15 | 0.001587 |
| 2008-05-01 | 15 | 0.001587 |
| 2006-11-01 | 15 | 0.001587 |
| 2006-07-01 | 15 | 0.001587 |
| 1993-01-07 | 14 | 0.001481 |
| 1992-12-01 | 14 | 0.001481 |
| 1993-01-01 | 14 | 0.001481 |
| 1995-01-01 | 14 | 0.001481 |
| 2000-01-01 | 14 | 0.001481 |
| 2007-10-01 | 13 | 0.001375 |
| 2007-03-01 | 13 | 0.001375 |
| 2005-08-01 | 13 | 0.001375 |
| 2008-01-01 | 13 | 0.001375 |
| 1993-05-01 | 13 | 0.001375 |
| 1996-06-01 | 13 | 0.001375 |
| 1994-03-01 | 13 | 0.001375 |
| 2009-07-01 | 13 | 0.001375 |
| 2006-10-01 | 12 | 0.001269 |
| 1996-01-01 | 12 | 0.001269 |
| 2006-05-01 | 12 | 0.001269 |
| 2000-03-01 | 12 | 0.001269 |
| 2007-04-01 | 11 | 0.001164 |
| 2006-09-01 | 11 | 0.001164 |
| 2010-10-01 | 11 | 0.001164 |
| 2006-06-01 | 11 | 0.001164 |
| 1993-08-05 | 10 | 0.001058 |
| 2007-09-01 | 10 | 0.001058 |
| 2007-06-01 | 10 | 0.001058 |
| 2007-01-01 | 10 | 0.001058 |
| 2009-11-01 | 10 | 0.001058 |
| 2007-12-01 | 10 | 0.001058 |
| 2005-09-01 | 9 | 0.000952 |
| 1993-11-01 | 9 | 0.000952 |
| 1994-01-01 | 9 | 0.000952 |
| 2005-11-01 | 9 | 0.000952 |
| 2023-06-01 | 9 | 0.000952 |
| 1994-06-06 | 9 | 0.000952 |
| 2007-02-01 | 8 | 0.000846 |
| 1992-07-01 | 8 | 0.000846 |
| 2022-12-01 | 8 | 0.000846 |
| 1993-12-07 | 8 | 0.000846 |
| 2020-08-01 | 8 | 0.000846 |
| 1994-08-01 | 8 | 0.000846 |
| 2023-07-03 | 8 | 0.000846 |
| 1993-02-17 | 8 | 0.000846 |
| 1992-04-02 | 7 | 0.000740 |
| 1994-05-09 | 7 | 0.000740 |
| 1993-09-16 | 7 | 0.000740 |
| 2007-07-01 | 7 | 0.000740 |
| 1992-10-14 | 7 | 0.000740 |
| 2007-05-01 | 7 | 0.000740 |
| 1992-12-14 | 6 | 0.000635 |
| 1993-12-01 | 6 | 0.000635 |
| 2006-12-01 | 6 | 0.000635 |
| 1992-01-02 | 6 | 0.000635 |
| 2006-08-01 | 6 | 0.000635 |
| 2005-10-01 | 6 | 0.000635 |
| 2022-11-02 | 6 | 0.000635 |
| 1995-05-01 | 5 | 0.000529 |
| 1991-01-20 | 5 | 0.000529 |
| 1996-08-01 | 5 | 0.000529 |
| 1995-09-01 | 5 | 0.000529 |
| 1994-06-01 | 5 | 0.000529 |
| 1993-02-01 | 4 | 0.000423 |
| 2022-06-01 | 4 | 0.000423 |
| 1991-01-21 | 4 | 0.000423 |
| 2002-07-11 | 4 | 0.000423 |
| 2022-09-01 | 4 | 0.000423 |
| 1994-04-01 | 4 | 0.000423 |
| 1994-12-01 | 4 | 0.000423 |
| 1993-03-08 | 4 | 0.000423 |
| 1991-12-01 | 4 | 0.000423 |
| 1992-09-01 | 3 | 0.000317 |
| 1992-10-01 | 3 | 0.000317 |
| 2006-01-01 | 3 | 0.000317 |
| 1993-08-01 | 3 | 0.000317 |
| 1991-07-01 | 3 | 0.000317 |
| 1992-08-14 | 3 | 0.000317 |
| 1996-09-01 | 3 | 0.000317 |
| 2022-07-04 | 3 | 0.000317 |
| 1991-02-20 | 3 | 0.000317 |
| 1993-04-27 | 3 | 0.000317 |
| 1991-09-01 | 3 | 0.000317 |
| 2002-06-18 | 2 | 0.000212 |
| 1991-08-01 | 2 | 0.000212 |
| 2021-05-03 | 2 | 0.000212 |
| 2022-01-03 | 2 | 0.000212 |
| 2023-05-02 | 2 | 0.000212 |
| 1992-02-02 | 2 | 0.000212 |
| 2020-10-05 | 2 | 0.000212 |
| 2023-04-03 | 2 | 0.000212 |
| 2022-04-01 | 2 | 0.000212 |
| 2021-06-01 | 2 | 0.000212 |
| 1994-05-01 | 2 | 0.000212 |
| 1992-08-01 | 2 | 0.000212 |
| 2022-05-02 | 2 | 0.000212 |
| 2021-04-01 | 2 | 0.000212 |
| 1992-06-02 | 2 | 0.000212 |
| 1993-04-01 | 2 | 0.000212 |
| 1992-05-21 | 2 | 0.000212 |
| 1992-09-30 | 2 | 0.000212 |
| 1993-09-01 | 2 | 0.000212 |
| 1991-04-15 | 2 | 0.000212 |
| 1993-03-11 | 2 | 0.000212 |
| 1993-02-12 | 2 | 0.000212 |
| 1991-11-06 | 2 | 0.000212 |
| 1990-10-01 | 1 | 0.000106 |
| 1991-11-01 | 1 | 0.000106 |
| 1990-12-20 | 1 | 0.000106 |
| 1991-01-10 | 1 | 0.000106 |
| 1991-06-03 | 1 | 0.000106 |
| 1997-05-04 | 1 | 0.000106 |
| 1991-10-01 | 1 | 0.000106 |
| 1994-07-28 | 1 | 0.000106 |
| 1992-02-05 | 1 | 0.000106 |
| 1999-09-14 | 1 | 0.000106 |
| 1991-09-03 | 1 | 0.000106 |
| 1993-04-30 | 1 | 0.000106 |
| 1992-03-02 | 1 | 0.000106 |
| 1993-04-19 | 1 | 0.000106 |
| 1991-01-04 | 1 | 0.000106 |
| 1999-08-12 | 1 | 0.000106 |
| 1992-04-01 | 1 | 0.000106 |
| 2023-01-02 | 1 | 0.000106 |
| 2018-10-08 | 1 | 0.000106 |
| 1991-03-25 | 1 | 0.000106 |
| 1993-06-01 | 1 | 0.000106 |
| 1993-05-04 | 1 | 0.000106 |
| 1990-11-01 | 1 | 0.000106 |
| 2021-08-02 | 1 | 0.000106 |
| 1993-04-03 | 1 | 0.000106 |
| 2022-03-09 | 1 | 0.000106 |
| 2002-06-11 | 1 | 0.000106 |
| 2021-12-01 | 1 | 0.000106 |
| 2021-07-01 | 1 | 0.000106 |
| 2022-08-01 | 1 | 0.000106 |
| 2021-04-20 | 1 | 0.000106 |
# Vamos a realizar analisis por cada variable
var = "msf_datelastdonation__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastdonation__c es 1175962. Lo que supone un 65.20736445606927% El nº de vacios para la variable msf_datelastdonation__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2017-05-16 | 9885 | 1.575407 |
| 2022-12-02 | 7522 | 1.198807 |
| 2020-07-01 | 7461 | 1.189085 |
| 2023-03-02 | 6831 | 1.088680 |
| 2020-06-01 | 4537 | 0.723077 |
| ... | ... | ... |
| 1994-03-23 | 1 | 0.000159 |
| 1991-05-04 | 1 | 0.000159 |
| 2002-08-23 | 1 | 0.000159 |
| 1999-05-02 | 1 | 0.000159 |
| 1994-09-18 | 1 | 0.000159 |
10773 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_date__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npsp__largest_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_date__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npsp__first_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_entrydatecurrentrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrydatecurrentrecurringdonor__c es 809767. Lo que supone un 44.90176714340927% El nº de vacios para la variable msf_entrydatecurrentrecurringdonor__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2000-02-01 | 3949 | 0.397423 |
| 2004-01-01 | 3842 | 0.386654 |
| 1994-10-01 | 3293 | 0.331404 |
| 2000-01-01 | 3274 | 0.329492 |
| 1995-02-01 | 2918 | 0.293664 |
| ... | ... | ... |
| 2002-01-30 | 1 | 0.000101 |
| 2003-11-17 | 1 | 0.000101 |
| 2005-08-27 | 1 | 0.000101 |
| 2002-01-16 | 1 | 0.000101 |
| 2011-06-25 | 1 | 0.000101 |
7860 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_date__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npsp__last_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_firstentrydaterecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstentrydaterecurringdonor__c es 809975. Lo que supone un 44.913300791441145% El nº de vacios para la variable msf_firstentrydaterecurringdonor__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2004-01-01 | 4974 | 0.500682 |
| 2000-02-01 | 4594 | 0.462432 |
| 1994-10-01 | 3823 | 0.384823 |
| 2000-01-01 | 3804 | 0.382910 |
| 1995-02-01 | 3374 | 0.339627 |
| ... | ... | ... |
| 2003-02-04 | 1 | 0.000101 |
| 2003-07-11 | 1 | 0.000101 |
| 2004-08-12 | 1 | 0.000101 |
| 2003-01-07 | 1 | 0.000101 |
| 2010-04-24 | 1 | 0.000101 |
7926 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__firstclosedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__firstclosedate__c es 506557. Lo que supone un 28.088702625402085% El nº de vacios para la variable npo02__firstclosedate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2016-12-01 | 50715 | 3.910593 |
| 2015-12-02 | 50688 | 3.908511 |
| 2014-12-02 | 46621 | 3.594908 |
| 2017-12-04 | 45905 | 3.539698 |
| 2013-12-02 | 30590 | 2.358771 |
| ... | ... | ... |
| 1999-12-19 | 1 | 0.000077 |
| 1992-02-26 | 1 | 0.000077 |
| 1995-12-10 | 1 | 0.000077 |
| 1990-02-02 | 1 | 0.000077 |
| 2013-04-14 | 1 | 0.000077 |
10973 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastrecurringdonationdate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastrecurringdonationdate__c es 1231036. Lo que supone un 68.2612304738943% El nº de vacios para la variable msf_lastrecurringdonationdate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-03-12 | 2204 | 0.385057 |
| 2014-03-13 | 1942 | 0.339283 |
| 2023-05-10 | 1794 | 0.313426 |
| 2018-03-07 | 1616 | 0.282328 |
| 2018-04-09 | 1555 | 0.271671 |
| ... | ... | ... |
| 2018-08-25 | 1 | 0.000175 |
| 2006-10-22 | 1 | 0.000175 |
| 1993-04-19 | 1 | 0.000175 |
| 2008-04-20 | 1 | 0.000175 |
| 2016-04-09 | 1 | 0.000175 |
7032 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__lastclosedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastclosedate__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npo02__lastclosedate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "gender__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable gender__c es 0. Lo que supone un 0.0% El nº de vacios para la variable gender__c es 139747. Lo que supone un 7.749003420724746%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Female | 793486 | 43.998982 |
| Male | 605658 | 33.583876 |
| Other | 264425 | 14.662427 |
| 139747 | 7.749003 | |
| M | 66 | 0.003660 |
| H | 37 | 0.002052 |
# Vamos a realizar analisis por cada variable
var = "msf_languagepreferer__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_languagepreferer__c es 1. Lo que supone un 5.545023092248668e-05%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| ESP | 1607878 | 89.157206 |
| CAT | 178609 | 9.903910 |
| GAL | 11099 | 0.615442 |
| EUS | 5805 | 0.321889 |
| ING | 27 | 0.001497 |
| 1 | 0.000055 |
# Vamos a realizar analisis por cada variable
var = "npo02__largestamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__largestamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1803419 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__smallestamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1803419 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_amount__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npsp__first_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__lastoppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastoppamount__c es 3626. Lo que supone un 0.2010625373249367% El nº de vacios para la variable npo02__lastoppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.00 | 502942 | 27.944436 |
| 10.00 | 162997 | 9.056430 |
| 15.00 | 97374 | 5.410289 |
| 20.00 | 94104 | 5.228601 |
| 30.00 | 71623 | 3.979513 |
| ... | ... | ... |
| 78.28 | 1 | 0.000056 |
| 1674.76 | 1 | 0.000056 |
| 55.80 | 1 | 0.000056 |
| 275.03 | 1 | 0.000056 |
| 122.12 | 1 | 0.000056 |
10069 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_amount__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npsp__last_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquotachange__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_annualizedquotachange__c es 1145924. Lo que supone un 63.541750419619625% El nº de vacios para la variable msf_annualizedquotachange__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 48.00 | 207919 | 31.622902 |
| 0.00 | 118697 | 18.052913 |
| 24.00 | 46376 | 7.053438 |
| 72.00 | 45925 | 6.984844 |
| 60.00 | 43108 | 6.556400 |
| 120.00 | 28119 | 4.276687 |
| 36.00 | 27732 | 4.217827 |
| 84.00 | 22833 | 3.472726 |
| 50.00 | 10626 | 1.616134 |
| 30.00 | 7533 | 1.145712 |
| 144.00 | 7394 | 1.124571 |
| 40.00 | 5521 | 0.839702 |
| 25.00 | 5257 | 0.799550 |
| 45.00 | 4661 | 0.708903 |
| 20.00 | 3932 | 0.598027 |
| 108.00 | 3853 | 0.586012 |
| 28.00 | 3495 | 0.531563 |
| 15.00 | 3448 | 0.524415 |
| 10.00 | 3315 | 0.504186 |
| 64.00 | 3000 | 0.456277 |
| 96.00 | 2767 | 0.420840 |
| 35.88 | 2766 | 0.420688 |
| 35.00 | 2551 | 0.387988 |
| 12.00 | 2207 | 0.335668 |
| 100.00 | 1840 | 0.279850 |
| 70.00 | 1759 | 0.267531 |
| 8.00 | 1751 | 0.266314 |
| 7.00 | 1666 | 0.253386 |
| 5.00 | 1659 | 0.252321 |
| 20.04 | 1625 | 0.247150 |
| 52.00 | 1619 | 0.246238 |
| 90.00 | 1468 | 0.223272 |
| 56.00 | 1443 | 0.219469 |
| 240.00 | 1280 | 0.194678 |
| 6.00 | 1260 | 0.191636 |
| 2.00 | 1217 | 0.185096 |
| 29.90 | 1182 | 0.179773 |
| 18.00 | 1048 | 0.159393 |
| 47.80 | 989 | 0.150419 |
| 80.00 | 966 | 0.146921 |
| 55.00 | 953 | 0.144944 |
| 14.95 | 857 | 0.130343 |
| 132.00 | 853 | 0.129735 |
| 16.00 | 850 | 0.129279 |
| 88.00 | 755 | 0.114830 |
| 44.00 | 714 | 0.108594 |
| 47.76 | 658 | 0.100077 |
| 180.00 | 613 | 0.093233 |
| 65.00 | 613 | 0.093233 |
| 168.00 | 610 | 0.092776 |
| 32.00 | 592 | 0.090039 |
| 76.00 | 574 | 0.087301 |
| 119.40 | 528 | 0.080305 |
| 14.00 | 505 | 0.076807 |
| 17.00 | 503 | 0.076502 |
| 22.00 | 490 | 0.074525 |
| 59.64 | 451 | 0.068594 |
| 33.00 | 448 | 0.068137 |
| 42.00 | 410 | 0.062358 |
| 54.00 | 395 | 0.060077 |
| 44.85 | 388 | 0.059012 |
| 140.00 | 371 | 0.056426 |
| 192.00 | 351 | 0.053384 |
| 71.60 | 334 | 0.050799 |
| 27.00 | 325 | 0.049430 |
| 156.00 | 302 | 0.045932 |
| 4.00 | 301 | 0.045780 |
| 200.00 | 299 | 0.045476 |
| 34.00 | 267 | 0.040609 |
| 160.00 | 263 | 0.040000 |
| 32.88 | 231 | 0.035133 |
| 8.97 | 199 | 0.030266 |
| 11.00 | 198 | 0.030114 |
| 41.00 | 189 | 0.028745 |
| 21.00 | 188 | 0.028593 |
| 68.00 | 188 | 0.028593 |
| 360.00 | 181 | 0.027529 |
| 9.00 | 169 | 0.025704 |
| 44.80 | 167 | 0.025399 |
| 110.00 | 143 | 0.021749 |
| 23.92 | 141 | 0.021445 |
| 130.00 | 136 | 0.020685 |
| 59.75 | 133 | 0.020228 |
| 31.00 | 129 | 0.019620 |
| 300.00 | 126 | 0.019164 |
| 142.80 | 124 | 0.018859 |
| 55.76 | 109 | 0.016578 |
| 480.00 | 107 | 0.016274 |
| 26.00 | 106 | 0.016122 |
| 58.00 | 96 | 0.014601 |
| 3.00 | 96 | 0.014601 |
| 51.96 | 94 | 0.014297 |
| 19.00 | 89 | 0.013536 |
| 62.00 | 86 | 0.013080 |
| 47.88 | 85 | 0.012928 |
| 38.00 | 82 | 0.012472 |
| 17.94 | 79 | 0.012015 |
| 99.40 | 79 | 0.012015 |
| 17.15 | 72 | 0.010951 |
| 40.08 | 70 | 0.010646 |
| 11.96 | 66 | 0.010038 |
| 75.00 | 64 | 0.009734 |
| 104.00 | 59 | 0.008973 |
| 46.00 | 59 | 0.008973 |
| 49.70 | 58 | 0.008821 |
| 52.60 | 55 | 0.008365 |
| 47.84 | 54 | 0.008213 |
| 105.00 | 48 | 0.007300 |
| 89.50 | 46 | 0.006996 |
| 83.52 | 44 | 0.006692 |
| 53.00 | 44 | 0.006692 |
| 400.00 | 40 | 0.006084 |
| 13.00 | 40 | 0.006084 |
| 66.00 | 39 | 0.005932 |
| 46.85 | 36 | 0.005475 |
| 47.00 | 35 | 0.005323 |
| 37.00 | 35 | 0.005323 |
| 600.00 | 33 | 0.005019 |
| 35.76 | 33 | 0.005019 |
| 5.98 | 32 | 0.004867 |
| 95.00 | 31 | 0.004715 |
| 32.04 | 30 | 0.004563 |
| 43.00 | 29 | 0.004411 |
| 720.00 | 28 | 0.004259 |
| 49.00 | 26 | 0.003954 |
| 2.99 | 25 | 0.003802 |
| 51.00 | 24 | 0.003650 |
| 150.00 | 24 | 0.003650 |
| 35.88 | 22 | 0.003346 |
| 119.00 | 22 | 0.003346 |
| 15.96 | 22 | 0.003346 |
| 66.96 | 21 | 0.003194 |
| 63.72 | 20 | 0.003042 |
| 125.00 | 20 | 0.003042 |
| 178.20 | 20 | 0.003042 |
| 119.28 | 20 | 0.003042 |
| 55.68 | 19 | 0.002890 |
| 78.00 | 19 | 0.002890 |
| 320.00 | 18 | 0.002738 |
| 1200.00 | 17 | 0.002586 |
| 27.92 | 17 | 0.002586 |
| 139.20 | 17 | 0.002586 |
| 92.00 | 16 | 0.002433 |
| 118.99 | 16 | 0.002433 |
| 85.00 | 15 | 0.002281 |
| 112.00 | 15 | 0.002281 |
| 63.64 | 14 | 0.002129 |
| 121.80 | 14 | 0.002129 |
| 51.72 | 14 | 0.002129 |
| 228.00 | 13 | 0.001977 |
| 26.91 | 12 | 0.001825 |
| 40.86 | 12 | 0.001825 |
| 59.00 | 12 | 0.001825 |
| 107.04 | 11 | 0.001673 |
| 280.00 | 11 | 0.001673 |
| 107.40 | 11 | 0.001673 |
| 14.16 | 11 | 0.001673 |
| 118.56 | 10 | 0.001521 |
| 57.00 | 10 | 0.001521 |
| 166.56 | 10 | 0.001521 |
| 69.60 | 9 | 0.001369 |
| 36.87 | 9 | 0.001369 |
| 51.82 | 9 | 0.001369 |
| 29.00 | 8 | 0.001217 |
| 124.00 | 8 | 0.001217 |
| 56.64 | 8 | 0.001217 |
| 46.56 | 8 | 0.001217 |
| 20.93 | 8 | 0.001217 |
| 39.00 | 8 | 0.001217 |
| 71.64 | 7 | 0.001065 |
| 23.00 | 7 | 0.001065 |
| 237.60 | 7 | 0.001065 |
| 95.16 | 7 | 0.001065 |
| 119.40 | 6 | 0.000913 |
| 28.31 | 6 | 0.000913 |
| 39.88 | 6 | 0.000913 |
| 216.00 | 6 | 0.000913 |
| 29.76 | 6 | 0.000913 |
| 45.60 | 6 | 0.000913 |
| 420.00 | 6 | 0.000913 |
| 204.00 | 6 | 0.000913 |
| 115.00 | 5 | 0.000760 |
| 276.00 | 5 | 0.000760 |
| 960.00 | 5 | 0.000760 |
| 47.52 | 5 | 0.000760 |
| 116.00 | 5 | 0.000760 |
| 357.00 | 5 | 0.000760 |
| 59.64 | 5 | 0.000760 |
| 51.80 | 5 | 0.000760 |
| 1440.00 | 5 | 0.000760 |
| 126.00 | 5 | 0.000760 |
| 89.49 | 5 | 0.000760 |
| 94.00 | 5 | 0.000760 |
| 26.32 | 5 | 0.000760 |
| 220.00 | 4 | 0.000608 |
| 238.00 | 4 | 0.000608 |
| 97.92 | 4 | 0.000608 |
| 8.97 | 4 | 0.000608 |
| 19.95 | 4 | 0.000608 |
| 74.00 | 4 | 0.000608 |
| 135.00 | 4 | 0.000608 |
| 51.84 | 4 | 0.000608 |
| 6.58 | 4 | 0.000608 |
| 79.50 | 4 | 0.000608 |
| 47.64 | 4 | 0.000608 |
| 1000.00 | 4 | 0.000608 |
| 114.00 | 4 | 0.000608 |
| 63.00 | 4 | 0.000608 |
| 21.93 | 4 | 0.000608 |
| 47.76 | 4 | 0.000608 |
| 59.65 | 4 | 0.000608 |
| 44.64 | 4 | 0.000608 |
| 41.88 | 4 | 0.000608 |
| 67.00 | 4 | 0.000608 |
| 68.04 | 3 | 0.000456 |
| 714.00 | 3 | 0.000456 |
| 21.60 | 3 | 0.000456 |
| 16.80 | 3 | 0.000456 |
| 14.88 | 3 | 0.000456 |
| 260.00 | 3 | 0.000456 |
| 2400.00 | 3 | 0.000456 |
| 34.90 | 3 | 0.000456 |
| 106.00 | 3 | 0.000456 |
| 33.89 | 3 | 0.000456 |
| 128.00 | 3 | 0.000456 |
| 800.00 | 3 | 0.000456 |
| 82.00 | 3 | 0.000456 |
| 136.00 | 3 | 0.000456 |
| 25.04 | 3 | 0.000456 |
| 49.85 | 3 | 0.000456 |
| 129.25 | 3 | 0.000456 |
| 16.95 | 3 | 0.000456 |
| 59.76 | 3 | 0.000456 |
| 148.00 | 3 | 0.000456 |
| 55.92 | 2 | 0.000304 |
| 53.82 | 2 | 0.000304 |
| 145.00 | 2 | 0.000304 |
| 3.99 | 2 | 0.000304 |
| 780.00 | 2 | 0.000304 |
| 31.90 | 2 | 0.000304 |
| 17.34 | 2 | 0.000304 |
| 3.34 | 2 | 0.000304 |
| 32.88 | 2 | 0.000304 |
| 60.60 | 2 | 0.000304 |
| 38.76 | 2 | 0.000304 |
| 162.00 | 2 | 0.000304 |
| 75.60 | 2 | 0.000304 |
| 118.80 | 2 | 0.000304 |
| 13.96 | 2 | 0.000304 |
| 500.00 | 2 | 0.000304 |
| 356.40 | 2 | 0.000304 |
| 6.68 | 2 | 0.000304 |
| 83.00 | 2 | 0.000304 |
| 64.08 | 2 | 0.000304 |
| 14400.00 | 2 | 0.000304 |
| 288.00 | 2 | 0.000304 |
| 20.95 | 2 | 0.000304 |
| 43.88 | 2 | 0.000304 |
| 44.04 | 2 | 0.000304 |
| 74.04 | 2 | 0.000304 |
| 102.00 | 2 | 0.000304 |
| 33.48 | 2 | 0.000304 |
| 324.00 | 2 | 0.000304 |
| 44.90 | 2 | 0.000304 |
| 24.12 | 2 | 0.000304 |
| 540.00 | 2 | 0.000304 |
| 122.40 | 1 | 0.000152 |
| 35.76 | 1 | 0.000152 |
| 28.80 | 1 | 0.000152 |
| 264.00 | 1 | 0.000152 |
| 100.08 | 1 | 0.000152 |
| 13.60 | 1 | 0.000152 |
| 115.20 | 1 | 0.000152 |
| 83.60 | 1 | 0.000152 |
| 840.00 | 1 | 0.000152 |
| 154.00 | 1 | 0.000152 |
| 5.50 | 1 | 0.000152 |
| 63.60 | 1 | 0.000152 |
| 141.12 | 1 | 0.000152 |
| 384.00 | 1 | 0.000152 |
| 580.00 | 1 | 0.000152 |
| 86.00 | 1 | 0.000152 |
| 33.90 | 1 | 0.000152 |
| -720.00 | 1 | 0.000152 |
| 59.28 | 1 | 0.000152 |
| -96.00 | 1 | 0.000152 |
| 143.76 | 1 | 0.000152 |
| 39.60 | 1 | 0.000152 |
| 43.84 | 1 | 0.000152 |
| 29.85 | 1 | 0.000152 |
| 97.00 | 1 | 0.000152 |
| 4.50 | 1 | 0.000152 |
| 202.20 | 1 | 0.000152 |
| 41.16 | 1 | 0.000152 |
| 440.00 | 1 | 0.000152 |
| 87.00 | 1 | 0.000152 |
| 34.99 | 1 | 0.000152 |
| 14.50 | 1 | 0.000152 |
| 475.20 | 1 | 0.000152 |
| 6.98 | 1 | 0.000152 |
| 81.52 | 1 | 0.000152 |
| 71.76 | 1 | 0.000152 |
| 51.96 | 1 | 0.000152 |
| 55.68 | 1 | 0.000152 |
| 44.25 | 1 | 0.000152 |
| 60.48 | 1 | 0.000152 |
| 51.77 | 1 | 0.000152 |
| 59.70 | 1 | 0.000152 |
| 29.95 | 1 | 0.000152 |
| 65.76 | 1 | 0.000152 |
| 3600.00 | 1 | 0.000152 |
| 81.12 | 1 | 0.000152 |
| 138.00 | 1 | 0.000152 |
| 51.85 | 1 | 0.000152 |
| 190.80 | 1 | 0.000152 |
| 107.40 | 1 | 0.000152 |
| 237.96 | 1 | 0.000152 |
| 560.00 | 1 | 0.000152 |
| 277.60 | 1 | 0.000152 |
| 8.66 | 1 | 0.000152 |
| 49.85 | 1 | 0.000152 |
| 1920.00 | 1 | 0.000152 |
| 49.90 | 1 | 0.000152 |
| 80.04 | 1 | 0.000152 |
| 296.97 | 1 | 0.000152 |
| 52.64 | 1 | 0.000152 |
| 135.44 | 1 | 0.000152 |
| 64.65 | 1 | 0.000152 |
| 27.88 | 1 | 0.000152 |
| 60.04 | 1 | 0.000152 |
| 41.86 | 1 | 0.000152 |
| 28.68 | 1 | 0.000152 |
| 17.95 | 1 | 0.000152 |
| -192.00 | 1 | 0.000152 |
| 109.25 | 1 | 0.000152 |
| 52.78 | 1 | 0.000152 |
| 64.32 | 1 | 0.000152 |
| 900.00 | 1 | 0.000152 |
| 59.85 | 1 | 0.000152 |
| -168.00 | 1 | 0.000152 |
| 43.86 | 1 | 0.000152 |
| 29.99 | 1 | 0.000152 |
| 713.88 | 1 | 0.000152 |
| 32.10 | 1 | 0.000152 |
| 59.80 | 1 | 0.000152 |
| 127.28 | 1 | 0.000152 |
| 131.88 | 1 | 0.000152 |
| 83.49 | 1 | 0.000152 |
| 57.36 | 1 | 0.000152 |
| 48.85 | 1 | 0.000152 |
| 47.83 | 1 | 0.000152 |
| 44.85 | 1 | 0.000152 |
| 179.00 | 1 | 0.000152 |
| 28.98 | 1 | 0.000152 |
| 24000.00 | 1 | 0.000152 |
| 139.00 | 1 | 0.000152 |
| 91.92 | 1 | 0.000152 |
| 178.80 | 1 | 0.000152 |
| 348.00 | 1 | 0.000152 |
| 16.08 | 1 | 0.000152 |
| 297.47 | 1 | 0.000152 |
| 166.68 | 1 | 0.000152 |
| 55.80 | 1 | 0.000152 |
| 77.00 | 1 | 0.000152 |
| 32.28 | 1 | 0.000152 |
| 67.76 | 1 | 0.000152 |
| 17.94 | 1 | 0.000152 |
| -108.00 | 1 | 0.000152 |
| 28.08 | 1 | 0.000152 |
| 52.08 | 1 | 0.000152 |
| 33.04 | 1 | 0.000152 |
| 32.14 | 1 | 0.000152 |
| 165.12 | 1 | 0.000152 |
| 432.00 | 1 | 0.000152 |
| 250.00 | 1 | 0.000152 |
| 15.92 | 1 | 0.000152 |
| 98.56 | 1 | 0.000152 |
| 129.00 | 1 | 0.000152 |
| 63.24 | 1 | 0.000152 |
| 71.88 | 1 | 0.000152 |
| 50.68 | 1 | 0.000152 |
| 61.68 | 1 | 0.000152 |
| 103.44 | 1 | 0.000152 |
| 119.20 | 1 | 0.000152 |
| 5.60 | 1 | 0.000152 |
| 297.60 | 1 | 0.000152 |
| 122.00 | 1 | 0.000152 |
| 87.52 | 1 | 0.000152 |
| 20.40 | 1 | 0.000152 |
| 143.40 | 1 | 0.000152 |
| 158.60 | 1 | 0.000152 |
| 236.00 | 1 | 0.000152 |
| 81.00 | 1 | 0.000152 |
| 52.88 | 1 | 0.000152 |
| 16.44 | 1 | 0.000152 |
| 56.04 | 1 | 0.000152 |
| 52.68 | 1 | 0.000152 |
| 35.40 | 1 | 0.000152 |
| 24.60 | 1 | 0.000152 |
| 257.57 | 1 | 0.000152 |
| 660.00 | 1 | 0.000152 |
| 52.80 | 1 | 0.000152 |
| 1600.00 | 1 | 0.000152 |
| 372.00 | 1 | 0.000152 |
| 69.00 | 1 | 0.000152 |
| 640.00 | 1 | 0.000152 |
| 53.64 | 1 | 0.000152 |
| 44.40 | 1 | 0.000152 |
| 67.64 | 1 | 0.000152 |
| 38.28 | 1 | 0.000152 |
| 350.00 | 1 | 0.000152 |
| 43.08 | 1 | 0.000152 |
| 10.50 | 1 | 0.000152 |
| 700.00 | 1 | 0.000152 |
| 37.90 | 1 | 0.000152 |
| 19.80 | 1 | 0.000152 |
| 63.76 | 1 | 0.000152 |
| 61.00 | 1 | 0.000152 |
| 63.80 | 1 | 0.000152 |
| 109.92 | 1 | 0.000152 |
| 15.95 | 1 | 0.000152 |
| 127.28 | 1 | 0.000152 |
| 147.72 | 1 | 0.000152 |
| 19.94 | 1 | 0.000152 |
| 64.75 | 1 | 0.000152 |
| 133.36 | 1 | 0.000152 |
| 1320.00 | 1 | 0.000152 |
| 66.84 | 1 | 0.000152 |
| 40.68 | 1 | 0.000152 |
| 197.98 | 1 | 0.000152 |
| 89.00 | 1 | 0.000152 |
# Vamos a realizar analisis por cada variable
var = "msf_relationshiplevel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_relationshiplevel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_relationshiplevel__c es 561. Lo que supone un 0.03110757954751503%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| a0l0O00000k727RQAQ | 1742956 | 96.647313 |
| a0l0O00000k727QQAQ | 31607 | 1.752615 |
| a0l0O00000k727SQAQ | 20157 | 1.117710 |
| a0l0O00000k727TQAQ | 6879 | 0.381442 |
| a0l0O00000k727UQAQ | 1259 | 0.069812 |
| 561 | 0.031108 |
# Vamos a realizar analisis por cada variable
var = "msf_ltvcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_ltvcont__c es 507098. Lo que supone un 28.11870120033115% El nº de vacios para la variable msf_ltvcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.0 | 47198 | 3.640919 |
| 10.0 | 29491 | 2.274977 |
| 30.0 | 26316 | 2.030053 |
| 20.0 | 24711 | 1.906241 |
| 60.0 | 22607 | 1.743935 |
| ... | ... | ... |
| 3076.1 | 1 | 0.000077 |
| 3660.1 | 1 | 0.000077 |
| 7904.0 | 1 | 0.000077 |
| 5073.1 | 1 | 0.000077 |
| 1628.7 | 1 | 0.000077 |
100092 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "mailingstate"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable mailingstate es 0. Lo que supone un 0.0% El nº de vacios para la variable mailingstate es 499399. Lo que supone un 27.691789872458923%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 499399 | 27.691790 | |
| MADRID | 227208 | 12.598736 |
| BARCELONA | 178733 | 9.910786 |
| VALENCIA/VALÈNCIA | 65690 | 3.642526 |
| BIZKAIA | 47719 | 2.646030 |
| ... | ... | ... |
| MAZOWIECKIE | 1 | 0.000055 |
| Castilla y la Mancha | 1 | 0.000055 |
| Wisconsin | 1 | 0.000055 |
| San Sebastián | 1 | 0.000055 |
| AvilA | 1 | 0.000055 |
1205 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_amount__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npsp__largest_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_last_year__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_last_year__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_last_year__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_this_year__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_this_year__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_this_year__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_two_years_ago__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_two_years_ago__c es 1803419. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_two_years_ago__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondoscp__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1449578 | 80.379435 |
| True | 353841 | 19.620565 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosemail__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1471944 | 81.619635 |
| True | 331475 | 18.380365 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosmi__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1518698 | 84.212155 |
| True | 284721 | 15.787845 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondossms__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1516147 | 84.070701 |
| True | 287272 | 15.929299 |
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaignentryrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaignentryrecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaignentryrecurringdonor__c es 809942. Lo que supone un 44.9114709338207%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 809942 | 44.911471 | |
| 7013Y000001mr4CQAQ | 37787 | 2.095298 |
| 7013Y000001mr2DQAQ | 31300 | 1.735592 |
| 7013Y000001mr2cQAA | 26419 | 1.464940 |
| 7013Y000001mrCzQAI | 25970 | 1.440042 |
| ... | ... | ... |
| 7013Y000001mrOuQAI | 1 | 0.000055 |
| 7013Y000001mrGjQAI | 1 | 0.000055 |
| 7013Y000001mrUjQAI | 1 | 0.000055 |
| 7013Y000001mqxTQAQ | 1 | 0.000055 |
| 7013Y000001mre3QAA | 1 | 0.000055 |
2565 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaingcolaboration__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaingcolaboration__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaingcolaboration__c es 511991. Lo que supone un 28.390019180234876%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 511991 | 28.390019 | |
| 7013Y000001mrCzQAI | 164897 | 9.143577 |
| 7013Y000001vYkXQAU | 44405 | 2.462268 |
| 7013Y000001mr4CQAQ | 34956 | 1.938318 |
| 7013Y000001mrBSQAY | 33346 | 1.849043 |
| ... | ... | ... |
| 7013Y000001mr26QAA | 1 | 0.000055 |
| 7013Y000001mr5dQAA | 1 | 0.000055 |
| 7013Y000001mr2dQAA | 1 | 0.000055 |
| 7013Y000001mrE4QAI | 1 | 0.000055 |
| 7013Y000001mrRkQAI | 1 | 0.000055 |
3747 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_firstannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstannualizedquota__c es 841860. Lo que supone un 46.68133140440464% El nº de vacios para la variable msf_firstannualizedquota__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 287648 | 29.914753 |
| 6.000000e+01 | 123651 | 12.859429 |
| 1.800000e+02 | 103083 | 10.720403 |
| 2.400000e+02 | 51256 | 5.330510 |
| 7.200000e+01 | 49342 | 5.131458 |
| 1.440000e+02 | 42521 | 4.422090 |
| 7.212000e+01 | 32909 | 3.422463 |
| 3.600000e+01 | 25308 | 2.631976 |
| 3.600000e+02 | 18085 | 1.880800 |
| 9.600000e+01 | 16441 | 1.709827 |
| 3.000000e+02 | 14049 | 1.461065 |
| 1.000000e+02 | 13112 | 1.363619 |
| 5.000000e+01 | 11678 | 1.214486 |
| 5.196000e+01 | 11000 | 1.143976 |
| 0.000000e+00 | 9343 | 0.971651 |
| 4.000000e+01 | 8834 | 0.918716 |
| 6.010000e+01 | 8598 | 0.894173 |
| 8.400000e+01 | 7852 | 0.816591 |
| 3.005000e+01 | 7602 | 0.790591 |
| 2.000000e+01 | 7382 | 0.767712 |
| 8.000000e+01 | 7025 | 0.730584 |
| 3.000000e+01 | 6974 | 0.725281 |
| 1.202000e+02 | 6514 | 0.677442 |
| 1.442400e+02 | 5434 | 0.565124 |
| 4.800000e+01 | 5204 | 0.541204 |
| 2.163600e+02 | 4876 | 0.507093 |
| 2.000000e+02 | 4820 | 0.501269 |
| 6.000000e+02 | 4229 | 0.439807 |
| 3.606000e+02 | 3825 | 0.397792 |
| 1.200000e+01 | 3772 | 0.392280 |
| 1.000000e+01 | 3465 | 0.360352 |
| 1.500000e+02 | 3268 | 0.339865 |
| 1.803000e+01 | 3166 | 0.329257 |
| 1.320000e+02 | 2999 | 0.311889 |
| 2.160000e+02 | 2293 | 0.238467 |
| 1.500000e+01 | 2293 | 0.238467 |
| 7.200000e+02 | 1972 | 0.205084 |
| 9.015000e+01 | 1774 | 0.184492 |
| 2.404000e+02 | 1725 | 0.179396 |
| 2.500000e+01 | 1672 | 0.173884 |
| 1.080000e+02 | 1569 | 0.163173 |
| 9.000000e+01 | 1565 | 0.162757 |
| 4.800000e+02 | 1354 | 0.140813 |
| 4.808000e+01 | 1208 | 0.125629 |
| 2.400000e+01 | 1120 | 0.116478 |
| 1.200000e+03 | 1049 | 0.109094 |
| 2.404000e+01 | 1043 | 0.108470 |
| 3.486000e+01 | 1041 | 0.108262 |
| 1.600000e+02 | 939 | 0.097654 |
| 1.560000e+02 | 856 | 0.089022 |
| 2.040000e+02 | 850 | 0.088398 |
| 4.000000e+02 | 814 | 0.084654 |
| 1.502500e+02 | 781 | 0.081222 |
| 7.212000e+02 | 779 | 0.081014 |
| 1.394400e+02 | 744 | 0.077374 |
| 3.606000e+01 | 724 | 0.075294 |
| 3.612000e+01 | 713 | 0.074150 |
| 1.082400e+02 | 652 | 0.067807 |
| 1.920000e+02 | 627 | 0.065207 |
| 1.040400e+02 | 598 | 0.062191 |
| 7.000000e+01 | 534 | 0.055535 |
| 1.803600e+02 | 503 | 0.052311 |
| 7.500000e+01 | 439 | 0.045655 |
| 6.010000e+00 | 382 | 0.039727 |
| 2.500000e+02 | 377 | 0.039207 |
| 1.730400e+02 | 376 | 0.039103 |
| 1.680000e+02 | 376 | 0.039103 |
| 4.200000e+02 | 353 | 0.036711 |
| 9.316000e+01 | 344 | 0.035775 |
| 1.202000e+01 | 341 | 0.035463 |
| 5.000000e+02 | 306 | 0.031823 |
| 2.884800e+02 | 304 | 0.031615 |
| 1.039200e+02 | 287 | 0.029847 |
| 5.000000e+00 | 273 | 0.028391 |
| 2.520000e+02 | 270 | 0.028079 |
| 9.616000e+01 | 262 | 0.027247 |
| 3.608000e+01 | 260 | 0.027039 |
| 7.224000e+01 | 254 | 0.026415 |
| 2.640000e+02 | 248 | 0.025791 |
| 1.803000e+02 | 248 | 0.025791 |
| 5.768000e+01 | 243 | 0.025271 |
| 2.880000e+02 | 243 | 0.025271 |
| 1.400000e+02 | 240 | 0.024959 |
| 5.200000e+01 | 229 | 0.023815 |
| 3.005100e+02 | 204 | 0.021216 |
| 4.183200e+02 | 204 | 0.021216 |
| 1.800000e+01 | 193 | 0.020072 |
| 1.000000e+03 | 177 | 0.018408 |
| 3.000000e+00 | 176 | 0.018304 |
| 6.000000e+00 | 146 | 0.015184 |
| 3.500000e+01 | 141 | 0.014664 |
| 6.012000e+01 | 141 | 0.014664 |
| 5.400000e+02 | 140 | 0.014560 |
| 2.885000e+01 | 138 | 0.014352 |
| 1.800000e+03 | 138 | 0.014352 |
| 4.207000e+01 | 136 | 0.014144 |
| 3.200000e+01 | 133 | 0.013832 |
| 4.320000e+02 | 116 | 0.012064 |
| 1.154000e+02 | 114 | 0.011856 |
| 1.250000e+02 | 112 | 0.011648 |
| 3.462000e+02 | 108 | 0.011232 |
| 1.442000e+01 | 102 | 0.010608 |
| 8.414000e+01 | 100 | 0.010400 |
| 1.080000e+03 | 99 | 0.010296 |
| 4.500000e+01 | 95 | 0.009880 |
| 1.923200e+02 | 89 | 0.009256 |
| 1.081800e+03 | 86 | 0.008944 |
| 5.770000e+01 | 86 | 0.008944 |
| 5.409000e+01 | 80 | 0.008320 |
| 2.400000e+03 | 80 | 0.008320 |
| 4.327200e+02 | 77 | 0.008008 |
| 8.000000e+02 | 75 | 0.007800 |
| 4.200000e+01 | 75 | 0.007800 |
| 6.010000e+02 | 71 | 0.007384 |
| 9.600000e+02 | 70 | 0.007280 |
| 6.010100e+02 | 68 | 0.007072 |
| 1.300000e+02 | 66 | 0.006864 |
| 9.000000e+02 | 64 | 0.006656 |
| 8.400000e+02 | 62 | 0.006448 |
| 3.960000e+02 | 61 | 0.006344 |
| 4.808000e+02 | 61 | 0.006344 |
| 2.760000e+02 | 60 | 0.006240 |
| 1.440000e+03 | 60 | 0.006240 |
| 8.000000e+00 | 60 | 0.006240 |
| 3.120000e+02 | 59 | 0.006136 |
| 1.500000e+03 | 55 | 0.005720 |
| 5.500000e+01 | 54 | 0.005616 |
| 1.081800e+02 | 53 | 0.005512 |
| 3.726400e+02 | 52 | 0.005408 |
| 5.769600e+02 | 48 | 0.004992 |
| 1.100000e+02 | 47 | 0.004888 |
| 1.204000e+01 | 46 | 0.004784 |
| 3.600000e+03 | 46 | 0.004784 |
| 1.803200e+02 | 45 | 0.004680 |
| 1.682800e+02 | 45 | 0.004680 |
| 2.404100e+02 | 43 | 0.004472 |
| 3.614400e+02 | 41 | 0.004264 |
| 3.240000e+02 | 41 | 0.004264 |
| 6.500000e+01 | 40 | 0.004160 |
| 3.200000e+02 | 39 | 0.004056 |
| 5.048400e+02 | 39 | 0.004056 |
| 1.442400e+03 | 39 | 0.004056 |
| 2.524800e+02 | 39 | 0.004056 |
| 5.400000e+01 | 38 | 0.003952 |
| 2.280000e+02 | 38 | 0.003952 |
| 3.840000e+02 | 37 | 0.003848 |
| 1.600000e+01 | 36 | 0.003744 |
| 8.654400e+02 | 36 | 0.003744 |
| 2.800000e+02 | 36 | 0.003744 |
| 1.082000e+02 | 36 | 0.003744 |
| 8.460000e+01 | 36 | 0.003744 |
| 2.000000e+03 | 33 | 0.003432 |
| 9.020000e+00 | 33 | 0.003432 |
| 2.200000e+02 | 32 | 0.003328 |
| 2.800000e+01 | 32 | 0.003328 |
| 3.650000e+02 | 30 | 0.003120 |
| 1.204800e+02 | 30 | 0.003120 |
| 3.360000e+02 | 30 | 0.003120 |
| 3.000000e+03 | 30 | 0.003120 |
| 3.500000e+02 | 29 | 0.003016 |
| 1.040000e+02 | 29 | 0.003016 |
| 1.094400e+02 | 27 | 0.002808 |
| 6.924000e+02 | 26 | 0.002704 |
| 6.000000e+03 | 26 | 0.002704 |
| 8.416000e+01 | 25 | 0.002600 |
| 3.720000e+02 | 25 | 0.002600 |
| 3.606100e+02 | 24 | 0.002496 |
| 7.813000e+01 | 24 | 0.002496 |
| 8.800000e+01 | 24 | 0.002496 |
| 5.040000e+02 | 23 | 0.002392 |
| 1.700000e+02 | 23 | 0.002392 |
| 2.600000e+02 | 23 | 0.002392 |
| 1.803000e+03 | 23 | 0.002392 |
| 6.024000e+01 | 22 | 0.002288 |
| 6.600000e+01 | 22 | 0.002288 |
| 5.600000e+01 | 22 | 0.002288 |
| 1.503000e+01 | 22 | 0.002288 |
| 3.900000e+01 | 21 | 0.002184 |
| 3.010000e+00 | 20 | 0.002080 |
| 4.680000e+02 | 20 | 0.002080 |
| 9.200000e+01 | 18 | 0.001872 |
| 2.160000e+01 | 18 | 0.001872 |
| 1.750000e+02 | 18 | 0.001872 |
| 3.800000e+01 | 18 | 0.001872 |
| 8.652000e+01 | 17 | 0.001768 |
| 1.824000e+02 | 17 | 0.001768 |
| 6.600000e+02 | 17 | 0.001768 |
| 8.500000e+01 | 16 | 0.001664 |
| 2.308000e+02 | 16 | 0.001664 |
| 2.103500e+02 | 16 | 0.001664 |
| 7.800000e+01 | 16 | 0.001664 |
| 4.400000e+01 | 16 | 0.001664 |
| 4.080000e+02 | 15 | 0.001560 |
| 1.520000e+02 | 15 | 0.001560 |
| 6.800000e+01 | 14 | 0.001456 |
| 8.640000e+02 | 14 | 0.001456 |
| 6.400000e+01 | 13 | 0.001352 |
| 3.012000e+01 | 13 | 0.001352 |
| 3.005000e+02 | 13 | 0.001352 |
| 1.200000e+04 | 13 | 0.001352 |
| 1.400000e+01 | 13 | 0.001352 |
| 6.120000e+02 | 13 | 0.001352 |
| 2.200000e+01 | 13 | 0.001352 |
| 1.201200e+02 | 12 | 0.001248 |
| 6.240000e+02 | 12 | 0.001248 |
| 7.000000e+00 | 12 | 0.001248 |
| 1.020000e+02 | 12 | 0.001248 |
| 4.000000e+00 | 12 | 0.001248 |
| 4.500000e+02 | 12 | 0.001248 |
| 1.719600e+02 | 12 | 0.001248 |
| 9.036000e+01 | 11 | 0.001144 |
| 3.606120e+03 | 11 | 0.001144 |
| 1.480000e+02 | 11 | 0.001144 |
| 5.760000e+02 | 11 | 0.001144 |
| 7.200000e+00 | 11 | 0.001144 |
| 1.202040e+03 | 11 | 0.001144 |
| 7.600000e+01 | 10 | 0.001040 |
| 4.332000e+01 | 10 | 0.001040 |
| 1.120000e+02 | 10 | 0.001040 |
| 7.920000e+02 | 10 | 0.001040 |
| 7.210000e+00 | 10 | 0.001040 |
| 9.012000e+01 | 9 | 0.000936 |
| 1.450000e+02 | 9 | 0.000936 |
| 4.508000e+01 | 9 | 0.000936 |
| 2.600000e+01 | 9 | 0.000936 |
| 1.280000e+02 | 9 | 0.000936 |
| 3.005200e+02 | 9 | 0.000936 |
| 3.480000e+02 | 9 | 0.000936 |
| 2.160000e+03 | 9 | 0.000936 |
| 9.000000e+00 | 8 | 0.000832 |
| 7.400000e+01 | 8 | 0.000832 |
| 5.289000e+01 | 8 | 0.000832 |
| 3.400000e+01 | 8 | 0.000832 |
| 7.228800e+02 | 8 | 0.000832 |
| 2.100000e+02 | 8 | 0.000832 |
| 7.800000e+02 | 7 | 0.000728 |
| 5.772000e+01 | 7 | 0.000728 |
| 6.200000e+01 | 7 | 0.000728 |
| 6.490800e+02 | 7 | 0.000728 |
| 7.300000e+01 | 7 | 0.000728 |
| 1.300000e+01 | 7 | 0.000728 |
| 5.300000e+01 | 7 | 0.000728 |
| 4.560000e+02 | 7 | 0.000728 |
| 3.365600e+02 | 7 | 0.000728 |
| 1.050000e+02 | 7 | 0.000728 |
| 6.611000e+01 | 7 | 0.000728 |
| 1.117920e+03 | 7 | 0.000728 |
| 1.350000e+02 | 7 | 0.000728 |
| 9.016000e+01 | 6 | 0.000624 |
| 1.160000e+02 | 6 | 0.000624 |
| 4.800000e+03 | 6 | 0.000624 |
| 1.020000e+03 | 6 | 0.000624 |
| 1.444000e+01 | 6 | 0.000624 |
| 7.932000e+01 | 6 | 0.000624 |
| 7.000000e+02 | 6 | 0.000624 |
| 5.052000e+01 | 6 | 0.000624 |
| 5.200000e+02 | 6 | 0.000624 |
| 2.250000e+02 | 6 | 0.000624 |
| 1.600000e+03 | 6 | 0.000624 |
| 2.163600e+03 | 6 | 0.000624 |
| 2.880000e+01 | 6 | 0.000624 |
| 7.212200e+02 | 6 | 0.000624 |
| 1.000000e+00 | 6 | 0.000624 |
| 3.700000e+01 | 5 | 0.000520 |
| 9.360000e+02 | 5 | 0.000520 |
| 9.996000e+01 | 5 | 0.000520 |
| 2.300000e+02 | 5 | 0.000520 |
| 1.100000e+01 | 5 | 0.000520 |
| 1.920000e+03 | 5 | 0.000520 |
| 1.240000e+02 | 5 | 0.000520 |
| 7.200000e-01 | 5 | 0.000520 |
| 1.320000e+03 | 5 | 0.000520 |
| 9.900000e+01 | 5 | 0.000520 |
| 5.409600e+02 | 5 | 0.000520 |
| 2.164000e+01 | 5 | 0.000520 |
| 1.440000e+01 | 5 | 0.000520 |
| 9.015200e+02 | 5 | 0.000520 |
| 7.200000e+03 | 5 | 0.000520 |
| 2.115600e+02 | 5 | 0.000520 |
| 4.400000e+02 | 4 | 0.000416 |
| 9.375600e+02 | 4 | 0.000416 |
| 1.560000e+03 | 4 | 0.000416 |
| 5.000000e+03 | 4 | 0.000416 |
| 1.360000e+02 | 4 | 0.000416 |
| 1.620000e+02 | 4 | 0.000416 |
| 9.496000e+01 | 4 | 0.000416 |
| 5.592000e+01 | 4 | 0.000416 |
| 4.507600e+02 | 4 | 0.000416 |
| 3.300000e+01 | 4 | 0.000416 |
| 1.700000e+01 | 4 | 0.000416 |
| 2.700000e+01 | 4 | 0.000416 |
| 5.412000e+01 | 4 | 0.000416 |
| 1.260000e+02 | 4 | 0.000416 |
| 6.400000e+02 | 4 | 0.000416 |
| 1.081200e+02 | 4 | 0.000416 |
| 1.400000e+03 | 4 | 0.000416 |
| 8.660000e+00 | 4 | 0.000416 |
| 1.250000e+01 | 4 | 0.000416 |
| 2.100000e+01 | 4 | 0.000416 |
| 2.404040e+03 | 4 | 0.000416 |
| 1.650000e+02 | 4 | 0.000416 |
| 2.409600e+02 | 4 | 0.000416 |
| 7.560000e+02 | 4 | 0.000416 |
| 1.502400e+02 | 4 | 0.000416 |
| 2.700000e+02 | 4 | 0.000416 |
| 2.300000e+01 | 4 | 0.000416 |
| 4.330000e+00 | 4 | 0.000416 |
| 4.000000e+03 | 3 | 0.000312 |
| 3.100000e+01 | 3 | 0.000312 |
| 4.207100e+02 | 3 | 0.000312 |
| 4.328000e+01 | 3 | 0.000312 |
| 1.081840e+03 | 3 | 0.000312 |
| 2.705000e+01 | 3 | 0.000312 |
| 9.616400e+02 | 3 | 0.000312 |
| 5.202000e+01 | 3 | 0.000312 |
| 7.500000e+02 | 3 | 0.000312 |
| 2.040000e+03 | 3 | 0.000312 |
| 1.226400e+02 | 3 | 0.000312 |
| 3.900000e+03 | 3 | 0.000312 |
| 2.104000e+01 | 3 | 0.000312 |
| 1.129900e+02 | 3 | 0.000312 |
| 7.513000e+01 | 3 | 0.000312 |
| 2.884920e+03 | 3 | 0.000312 |
| 1.009680e+03 | 3 | 0.000312 |
| 3.004000e+01 | 3 | 0.000312 |
| 3.330000e+02 | 3 | 0.000312 |
| 6.972000e+01 | 3 | 0.000312 |
| 3.996000e+01 | 3 | 0.000312 |
| 1.804000e+01 | 3 | 0.000312 |
| 1.803040e+03 | 3 | 0.000312 |
| 3.846400e+02 | 3 | 0.000312 |
| 2.884000e+01 | 3 | 0.000312 |
| 2.750000e+02 | 3 | 0.000312 |
| 4.440000e+02 | 3 | 0.000312 |
| 1.732000e+01 | 3 | 0.000312 |
| 1.212000e+03 | 3 | 0.000312 |
| 1.510000e+02 | 3 | 0.000312 |
| 3.125200e+02 | 3 | 0.000312 |
| 2.004000e+03 | 3 | 0.000312 |
| 3.400000e+02 | 3 | 0.000312 |
| 1.900000e+02 | 3 | 0.000312 |
| 6.360000e+02 | 3 | 0.000312 |
| 9.372000e+01 | 3 | 0.000312 |
| 5.100000e+01 | 3 | 0.000312 |
| 1.983300e+02 | 3 | 0.000312 |
| 6.480000e+02 | 3 | 0.000312 |
| 1.532600e+02 | 3 | 0.000312 |
| 1.983600e+02 | 3 | 0.000312 |
| 4.600000e+02 | 2 | 0.000208 |
| 1.800000e+04 | 2 | 0.000208 |
| 3.750000e+02 | 2 | 0.000208 |
| 5.988000e+01 | 2 | 0.000208 |
| 3.660000e+02 | 2 | 0.000208 |
| 1.280200e+02 | 2 | 0.000208 |
| 7.356000e+02 | 2 | 0.000208 |
| 3.666000e+01 | 2 | 0.000208 |
| 7.933200e+02 | 2 | 0.000208 |
| 2.884900e+02 | 2 | 0.000208 |
| 1.830000e+02 | 2 | 0.000208 |
| 1.850000e+02 | 2 | 0.000208 |
| 1.210000e+02 | 2 | 0.000208 |
| 4.800000e+00 | 2 | 0.000208 |
| 2.480000e+02 | 2 | 0.000208 |
| 4.600000e+01 | 2 | 0.000208 |
| 8.414000e+02 | 2 | 0.000208 |
| 1.322400e+02 | 2 | 0.000208 |
| 4.327000e+01 | 2 | 0.000208 |
| 1.200100e+02 | 2 | 0.000208 |
| 6.100000e+01 | 2 | 0.000208 |
| 2.644400e+02 | 2 | 0.000208 |
| 6.492000e+01 | 2 | 0.000208 |
| 1.640000e+02 | 2 | 0.000208 |
| 4.920000e+02 | 2 | 0.000208 |
| 5.500000e+02 | 2 | 0.000208 |
| 3.250000e+02 | 2 | 0.000208 |
| 2.520000e+01 | 2 | 0.000208 |
| 8.200000e+01 | 2 | 0.000208 |
| 1.502600e+02 | 2 | 0.000208 |
| 2.406000e+02 | 2 | 0.000208 |
| 7.440000e+01 | 2 | 0.000208 |
| 1.010000e+02 | 2 | 0.000208 |
| 6.500000e+02 | 2 | 0.000208 |
| 2.019600e+02 | 2 | 0.000208 |
| 2.403600e+02 | 2 | 0.000208 |
| 3.602400e+02 | 2 | 0.000208 |
| 4.200000e+03 | 2 | 0.000208 |
| 1.622400e+02 | 2 | 0.000208 |
| 8.700000e+01 | 2 | 0.000208 |
| 7.200000e+04 | 2 | 0.000208 |
| 3.006000e+01 | 2 | 0.000208 |
| 3.300000e+02 | 2 | 0.000208 |
| 1.802800e+02 | 2 | 0.000208 |
| 2.598000e+01 | 2 | 0.000208 |
| 9.999600e+02 | 2 | 0.000208 |
| 2.000000e+00 | 2 | 0.000208 |
| 7.700000e+01 | 2 | 0.000208 |
| 9.320000e+00 | 2 | 0.000208 |
| 2.900000e+01 | 2 | 0.000208 |
| 7.250000e+02 | 2 | 0.000208 |
| 1.202020e+03 | 2 | 0.000208 |
| 1.021700e+02 | 2 | 0.000208 |
| 4.808100e+02 | 2 | 0.000208 |
| 1.150000e+02 | 2 | 0.000208 |
| 9.500000e+01 | 2 | 0.000208 |
| 1.959600e+02 | 2 | 0.000208 |
| 5.520000e+02 | 2 | 0.000208 |
| 3.900000e+02 | 2 | 0.000208 |
| 1.008000e+03 | 2 | 0.000208 |
| 1.230000e+02 | 2 | 0.000208 |
| 1.586400e+02 | 2 | 0.000208 |
| 7.572000e+01 | 2 | 0.000208 |
| 1.382300e+02 | 2 | 0.000208 |
| 1.460000e+02 | 2 | 0.000208 |
| 7.212120e+03 | 2 | 0.000208 |
| 1.444800e+02 | 2 | 0.000208 |
| 4.300000e+01 | 2 | 0.000208 |
| 6.396000e+01 | 2 | 0.000208 |
| 4.519600e+02 | 2 | 0.000208 |
| 6.960000e+02 | 2 | 0.000208 |
| 1.740000e+02 | 2 | 0.000208 |
| 5.880000e+02 | 2 | 0.000208 |
| 8.656000e+01 | 2 | 0.000208 |
| 7.452000e+01 | 2 | 0.000208 |
| 4.320000e+03 | 2 | 0.000208 |
| 1.394000e+02 | 2 | 0.000208 |
| 7.320000e+01 | 2 | 0.000208 |
| 2.220000e+02 | 2 | 0.000208 |
| 8.040000e+02 | 2 | 0.000208 |
| 6.200000e+02 | 2 | 0.000208 |
| 7.320000e+02 | 2 | 0.000208 |
| 1.100000e+03 | 2 | 0.000208 |
| 7.440000e+02 | 2 | 0.000208 |
| 1.260000e+03 | 2 | 0.000208 |
| 3.607200e+02 | 2 | 0.000208 |
| 1.382000e+01 | 2 | 0.000208 |
| 3.060000e+02 | 2 | 0.000208 |
| 1.000100e+02 | 2 | 0.000208 |
| 1.200000e+00 | 2 | 0.000208 |
| 2.440000e+02 | 2 | 0.000208 |
| 1.355880e+03 | 2 | 0.000208 |
| 1.684000e+01 | 2 | 0.000208 |
| 1.960000e+02 | 2 | 0.000208 |
| 1.562800e+02 | 2 | 0.000208 |
| 9.100000e+01 | 2 | 0.000208 |
| 2.061500e+02 | 2 | 0.000208 |
| 5.408000e+01 | 2 | 0.000208 |
| 7.992000e+01 | 2 | 0.000208 |
| 1.250000e+03 | 2 | 0.000208 |
| 2.880000e+03 | 2 | 0.000208 |
| 5.196000e+02 | 2 | 0.000208 |
| 1.110000e+02 | 2 | 0.000208 |
| 6.130800e+02 | 2 | 0.000208 |
| 8.160000e+02 | 2 | 0.000208 |
| 1.154400e+02 | 2 | 0.000208 |
| 3.246000e+02 | 2 | 0.000208 |
| 2.379600e+02 | 2 | 0.000208 |
| 1.262100e+02 | 2 | 0.000208 |
| 2.550000e+02 | 1 | 0.000104 |
| 8.640000e+01 | 1 | 0.000104 |
| 9.204000e+01 | 1 | 0.000104 |
| 5.280000e+02 | 1 | 0.000104 |
| 9.999000e+01 | 1 | 0.000104 |
| 5.600000e+02 | 1 | 0.000104 |
| 4.692000e+01 | 1 | 0.000104 |
| 5.280000e+01 | 1 | 0.000104 |
| 6.840000e+02 | 1 | 0.000104 |
| 9.324000e+01 | 1 | 0.000104 |
| 5.160000e+01 | 1 | 0.000104 |
| 6.060000e+01 | 1 | 0.000104 |
| 2.240000e+02 | 1 | 0.000104 |
| 6.000000e-01 | 1 | 0.000104 |
| 6.015000e+01 | 1 | 0.000104 |
| 9.840000e+03 | 1 | 0.000104 |
| 2.476800e+02 | 1 | 0.000104 |
| 3.110000e+02 | 1 | 0.000104 |
| 6.600000e+03 | 1 | 0.000104 |
| 6.800000e+02 | 1 | 0.000104 |
| 2.100000e+03 | 1 | 0.000104 |
| 2.560000e+02 | 1 | 0.000104 |
| 1.716000e+02 | 1 | 0.000104 |
| 9.015100e+02 | 1 | 0.000104 |
| 3.010000e+02 | 1 | 0.000104 |
| 1.900000e+01 | 1 | 0.000104 |
| 2.150000e+02 | 1 | 0.000104 |
| 5.800000e+01 | 1 | 0.000104 |
| 1.202400e+02 | 1 | 0.000104 |
| 5.908000e+01 | 1 | 0.000104 |
| 1.680000e+03 | 1 | 0.000104 |
| 1.665600e+02 | 1 | 0.000104 |
| 1.250400e+02 | 1 | 0.000104 |
| 1.159200e+02 | 1 | 0.000104 |
| 6.235200e+02 | 1 | 0.000104 |
| 1.442440e+03 | 1 | 0.000104 |
| 2.720000e+02 | 1 | 0.000104 |
| 2.439600e+02 | 1 | 0.000104 |
| 3.800000e+02 | 1 | 0.000104 |
| 1.000800e+02 | 1 | 0.000104 |
| 5.040000e+01 | 1 | 0.000104 |
| 3.350000e+02 | 1 | 0.000104 |
| 2.253800e+03 | 1 | 0.000104 |
| 3.040000e+01 | 1 | 0.000104 |
| 1.052400e+02 | 1 | 0.000104 |
| 1.893600e+02 | 1 | 0.000104 |
| 1.446000e+02 | 1 | 0.000104 |
| 5.100000e+02 | 1 | 0.000104 |
| 1.296000e+03 | 1 | 0.000104 |
| 5.700000e+01 | 1 | 0.000104 |
| 2.560000e+01 | 1 | 0.000104 |
| 3.320000e+02 | 1 | 0.000104 |
| 1.812000e+02 | 1 | 0.000104 |
| 3.726000e+01 | 1 | 0.000104 |
| 2.960000e+02 | 1 | 0.000104 |
| 1.470000e+02 | 1 | 0.000104 |
| 1.860000e+03 | 1 | 0.000104 |
| 5.288000e+01 | 1 | 0.000104 |
| 1.140000e+03 | 1 | 0.000104 |
| 6.720000e+01 | 1 | 0.000104 |
| 6.876000e+01 | 1 | 0.000104 |
| 9.912000e+01 | 1 | 0.000104 |
| 1.658400e+02 | 1 | 0.000104 |
| 2.999000e+01 | 1 | 0.000104 |
| 1.238000e+02 | 1 | 0.000104 |
| 1.452000e+02 | 1 | 0.000104 |
| 1.208000e+02 | 1 | 0.000104 |
| 2.050000e+02 | 1 | 0.000104 |
| 2.000400e+02 | 1 | 0.000104 |
| 6.016000e+01 | 1 | 0.000104 |
| 4.208000e+01 | 1 | 0.000104 |
| 2.180000e+02 | 1 | 0.000104 |
| 4.100000e+01 | 1 | 0.000104 |
| 1.002000e+03 | 1 | 0.000104 |
| 7.812000e+01 | 1 | 0.000104 |
| 3.954800e+02 | 1 | 0.000104 |
| 3.005060e+04 | 1 | 0.000104 |
| 2.920000e+02 | 1 | 0.000104 |
| 1.472500e+02 | 1 | 0.000104 |
| 1.478520e+03 | 1 | 0.000104 |
| 6.346800e+02 | 1 | 0.000104 |
| 4.095600e+02 | 1 | 0.000104 |
| 2.496000e+03 | 1 | 0.000104 |
| 4.992000e+01 | 1 | 0.000104 |
| 6.001000e+01 | 1 | 0.000104 |
| 5.900000e+01 | 1 | 0.000104 |
| 1.586640e+03 | 1 | 0.000104 |
| 4.700000e+01 | 1 | 0.000104 |
| 1.056000e+02 | 1 | 0.000104 |
| 1.340000e+02 | 1 | 0.000104 |
| 8.246000e+02 | 1 | 0.000104 |
| 1.089600e+02 | 1 | 0.000104 |
| 1.947600e+02 | 1 | 0.000104 |
| 2.310000e+02 | 1 | 0.000104 |
| 6.660000e+01 | 1 | 0.000104 |
| 4.116000e+01 | 1 | 0.000104 |
| 1.300000e+03 | 1 | 0.000104 |
| 3.768000e+02 | 1 | 0.000104 |
| 2.340000e+02 | 1 | 0.000104 |
| 1.420000e+02 | 1 | 0.000104 |
| 2.388000e+02 | 1 | 0.000104 |
| 2.850000e+02 | 1 | 0.000104 |
| 3.780000e+02 | 1 | 0.000104 |
| 9.400000e+01 | 1 | 0.000104 |
| 1.036800e+02 | 1 | 0.000104 |
| 3.906600e+02 | 1 | 0.000104 |
| 4.928400e+02 | 1 | 0.000104 |
| 1.080000e+01 | 1 | 0.000104 |
| 5.048000e+01 | 1 | 0.000104 |
| 9.600000e+00 | 1 | 0.000104 |
| 1.380000e+03 | 1 | 0.000104 |
| 9.720000e+01 | 1 | 0.000104 |
| 9.096000e+01 | 1 | 0.000104 |
| 1.002000e+02 | 1 | 0.000104 |
| 1.870000e+02 | 1 | 0.000104 |
| 1.027200e+02 | 1 | 0.000104 |
| 9.232000e+01 | 1 | 0.000104 |
| 1.268400e+02 | 1 | 0.000104 |
| 3.885000e+01 | 1 | 0.000104 |
| 1.298400e+02 | 1 | 0.000104 |
| 5.160000e+02 | 1 | 0.000104 |
| 3.305600e+02 | 1 | 0.000104 |
| 3.336000e+02 | 1 | 0.000104 |
| 1.033760e+03 | 1 | 0.000104 |
| 2.043600e+02 | 1 | 0.000104 |
| 1.284000e+03 | 1 | 0.000104 |
| 5.944800e+02 | 1 | 0.000104 |
| 4.688400e+02 | 1 | 0.000104 |
| 1.800000e+00 | 1 | 0.000104 |
| 7.510000e+00 | 1 | 0.000104 |
| 6.010200e+02 | 1 | 0.000104 |
| 9.020000e+01 | 1 | 0.000104 |
| 3.065200e+02 | 1 | 0.000104 |
| 4.028000e+01 | 1 | 0.000104 |
| 8.292000e+01 | 1 | 0.000104 |
| 2.456676e+07 | 1 | 0.000104 |
| 2.596800e+02 | 1 | 0.000104 |
| 1.430400e+02 | 1 | 0.000104 |
| 2.200000e+03 | 1 | 0.000104 |
| 6.132000e+01 | 1 | 0.000104 |
| 1.322200e+02 | 1 | 0.000104 |
| 2.704600e+02 | 1 | 0.000104 |
| 7.220000e+01 | 1 | 0.000104 |
| 1.200000e+05 | 1 | 0.000104 |
| 1.840000e+02 | 1 | 0.000104 |
| 1.710000e+02 | 1 | 0.000104 |
| 1.502530e+03 | 1 | 0.000104 |
| 1.202000e+03 | 1 | 0.000104 |
| 4.580000e+02 | 1 | 0.000104 |
| 5.949600e+02 | 1 | 0.000104 |
| 2.307600e+02 | 1 | 0.000104 |
| 5.109000e+01 | 1 | 0.000104 |
| 3.124000e+01 | 1 | 0.000104 |
| 3.100000e+02 | 1 | 0.000104 |
| 6.010120e+03 | 1 | 0.000104 |
| 1.620000e+03 | 1 | 0.000104 |
| 2.524400e+02 | 1 | 0.000104 |
| 1.060000e+02 | 1 | 0.000104 |
| 6.720000e+02 | 1 | 0.000104 |
| 9.012000e+02 | 1 | 0.000104 |
| 2.500000e+03 | 1 | 0.000104 |
| 9.600000e+03 | 1 | 0.000104 |
| 4.182000e+02 | 1 | 0.000104 |
| 1.045760e+03 | 1 | 0.000104 |
| 9.840000e+02 | 1 | 0.000104 |
| 1.552000e+01 | 1 | 0.000104 |
| 2.046000e+02 | 1 | 0.000104 |
| 1.146720e+03 | 1 | 0.000104 |
| 1.030000e+02 | 1 | 0.000104 |
| 1.875600e+02 | 1 | 0.000104 |
| 9.720000e+02 | 1 | 0.000104 |
| 9.240000e+00 | 1 | 0.000104 |
| 3.612000e+03 | 1 | 0.000104 |
| 2.524300e+02 | 1 | 0.000104 |
| 2.402000e+01 | 1 | 0.000104 |
| 1.083600e+02 | 1 | 0.000104 |
| 1.092000e+02 | 1 | 0.000104 |
| 6.242400e+02 | 1 | 0.000104 |
| 5.870000e+00 | 1 | 0.000104 |
| 2.928000e+02 | 1 | 0.000104 |
| 1.983200e+02 | 1 | 0.000104 |
| 2.760000e+03 | 1 | 0.000104 |
| 8.166000e+03 | 1 | 0.000104 |
| 2.803600e+02 | 1 | 0.000104 |
| 1.980000e+02 | 1 | 0.000104 |
| 1.524000e+03 | 1 | 0.000104 |
| 1.203000e+01 | 1 | 0.000104 |
| 8.052648e+09 | 1 | 0.000104 |
| 1.080000e+06 | 1 | 0.000104 |
| 1.164000e+06 | 1 | 0.000104 |
| 7.596000e+01 | 1 | 0.000104 |
| 2.058000e+04 | 1 | 0.000104 |
| 1.253400e+02 | 1 | 0.000104 |
| 5.400000e+03 | 1 | 0.000104 |
| 1.239600e+02 | 1 | 0.000104 |
| 1.939200e+02 | 1 | 0.000104 |
| 9.015240e+03 | 1 | 0.000104 |
| 2.636000e+01 | 1 | 0.000104 |
| 7.230000e+01 | 1 | 0.000104 |
| 1.046400e+02 | 1 | 0.000104 |
| 6.008000e+01 | 1 | 0.000104 |
| 1.104000e+02 | 1 | 0.000104 |
| 4.059600e+02 | 1 | 0.000104 |
| 2.957040e+03 | 1 | 0.000104 |
| 7.220000e+00 | 1 | 0.000104 |
| 1.009200e+02 | 1 | 0.000104 |
| 4.900000e+01 | 1 | 0.000104 |
| 2.425000e+02 | 1 | 0.000104 |
| 4.484000e+01 | 1 | 0.000104 |
| 5.152800e+02 | 1 | 0.000104 |
| 8.925000e+01 | 1 | 0.000104 |
| 2.956800e+02 | 1 | 0.000104 |
| 2.410000e+02 | 1 | 0.000104 |
| 7.080000e+02 | 1 | 0.000104 |
| 2.064000e+03 | 1 | 0.000104 |
| 1.550000e+02 | 1 | 0.000104 |
| 1.834800e+02 | 1 | 0.000104 |
| 2.170800e+02 | 1 | 0.000104 |
| 2.401200e+02 | 1 | 0.000104 |
| 9.800000e+01 | 1 | 0.000104 |
| 3.889200e+02 | 1 | 0.000104 |
| 2.184000e+03 | 1 | 0.000104 |
| 4.700000e+02 | 1 | 0.000104 |
| 1.008000e+04 | 1 | 0.000104 |
| 1.536000e+03 | 1 | 0.000104 |
| 1.009600e+02 | 1 | 0.000104 |
| 5.460000e+01 | 1 | 0.000104 |
| 1.001500e+02 | 1 | 0.000104 |
| 1.678800e+02 | 1 | 0.000104 |
| 6.005000e+01 | 1 | 0.000104 |
| 1.812000e+03 | 1 | 0.000104 |
| 1.298160e+03 | 1 | 0.000104 |
| 1.490400e+02 | 1 | 0.000104 |
| 3.604000e+01 | 1 | 0.000104 |
| 3.996000e+02 | 1 | 0.000104 |
| 7.620000e+01 | 1 | 0.000104 |
| 3.700000e+02 | 1 | 0.000104 |
| 9.960000e+01 | 1 | 0.000104 |
| 8.016000e+01 | 1 | 0.000104 |
| 1.975200e+02 | 1 | 0.000104 |
| 6.300000e+01 | 1 | 0.000104 |
| 2.199720e+03 | 1 | 0.000104 |
| 1.056000e+03 | 1 | 0.000104 |
| 1.002800e+02 | 1 | 0.000104 |
| 1.090000e+02 | 1 | 0.000104 |
| 1.188000e+02 | 1 | 0.000104 |
| 6.006000e+01 | 1 | 0.000104 |
| 1.802400e+02 | 1 | 0.000104 |
| 1.500000e+04 | 1 | 0.000104 |
| 1.514400e+02 | 1 | 0.000104 |
| 6.020000e+02 | 1 | 0.000104 |
| 1.500100e+02 | 1 | 0.000104 |
| 3.000100e+02 | 1 | 0.000104 |
| 2.524200e+03 | 1 | 0.000104 |
| 2.193600e+02 | 1 | 0.000104 |
| 2.900000e+02 | 1 | 0.000104 |
| 1.501500e+02 | 1 | 0.000104 |
| 2.451600e+02 | 1 | 0.000104 |
| 5.493000e+01 | 1 | 0.000104 |
| 8.655000e+01 | 1 | 0.000104 |
| 1.211640e+03 | 1 | 0.000104 |
| 2.352000e+03 | 1 | 0.000104 |
| 2.196000e+02 | 1 | 0.000104 |
| 1.203600e+02 | 1 | 0.000104 |
| 3.607000e+01 | 1 | 0.000104 |
| 8.880000e+02 | 1 | 0.000104 |
| 1.200800e+02 | 1 | 0.000104 |
| 8.280000e+02 | 1 | 0.000104 |
| 7.204000e+01 | 1 | 0.000104 |
| 1.201200e+04 | 1 | 0.000104 |
| 8.900000e+01 | 1 | 0.000104 |
| 3.480000e+03 | 1 | 0.000104 |
| 3.230000e+02 | 1 | 0.000104 |
| 1.236000e+03 | 1 | 0.000104 |
| 4.119600e+02 | 1 | 0.000104 |
| 1.436400e+02 | 1 | 0.000104 |
| 1.340000e+01 | 1 | 0.000104 |
| 2.282400e+03 | 1 | 0.000104 |
| 5.950000e+01 | 1 | 0.000104 |
| 4.520000e+02 | 1 | 0.000104 |
| 6.480000e+01 | 1 | 0.000104 |
| 1.599600e+02 | 1 | 0.000104 |
| 1.220000e+02 | 1 | 0.000104 |
# Vamos a realizar analisis por cada variable
var = "msf_program__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_program__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_program__c es 36959. Lo que supone un 2.049385084664185%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Reactivación bajas MASS | 473018 | 26.228957 |
| Cultivación socios MASS | 432996 | 24.009728 |
| Conversión prospectos | 402715 | 22.330640 |
| Reactivación/conversión EXDonantes MASS | 283100 | 15.697960 |
| Cultivación/conversión Donantes MASS | 61383 | 3.403702 |
| 36959 | 2.049385 | |
| Prospectos Empresas & Colectivos Mass | 30148 | 1.671714 |
| Empresas y Colectivos Mass | 26005 | 1.441983 |
| Retención 1r año MASS | 24156 | 1.339456 |
| Cultivación socios MID | 17102 | 0.948310 |
| Mid+ Donors | 5336 | 0.295882 |
| Instituciones Públicas Mass | 2447 | 0.135687 |
| Otros programas transversales | 1539 | 0.085338 |
| Cultivación/conversión Donantes MID | 1461 | 0.081013 |
| Testamentarios | 1170 | 0.064877 |
| Empresas y Colectivos Mid, Mid + | 762 | 0.042253 |
| Empresas y Colectivos Estratégicas | 653 | 0.036209 |
| Otros 12Few+ | 373 | 0.020683 |
| Reactivación bajas MID | 372 | 0.020627 |
| Fundaciones Mass | 293 | 0.016247 |
| Fundaciones Estratégicas | 246 | 0.013641 |
| Reactivación/conversión EXDonantes MID | 216 | 0.011977 |
| Vehículo donación de Gran Donante = YES | 215 | 0.011922 |
| Retención 1r año MID | 188 | 0.010425 |
| Prospectos Fundaciones Mass | 165 | 0.009149 |
| Públicos Especiales | 146 | 0.008096 |
| Major Donors | 115 | 0.006377 |
| Potenciales a Major Donors | 69 | 0.003826 |
| Otros 121 | 29 | 0.001608 |
| Fundaciones Mid, Mid + | 21 | 0.001164 |
| Instituciones Públicas Mid y Mid + | 20 | 0.001109 |
| Instituciones Públicas Estratégicas | 1 | 0.000055 |
# Vamos a realizar analisis por cada variable
var = "msf_programaherencias__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programaherencias__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1795692 | 99.571536 |
| True | 7727 | 0.428464 |
# Vamos a realizar analisis por cada variable
var = "msf_programais__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programais__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_programais__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1802909 | 99.97172 |
| True | 510 | 0.02828 |
# Vamos a realizar analisis por cada variable
var = "msf_pressurecomplaint__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 1798033 | 99.701345 |
| True | 5386 | 0.298655 |
# Vamos a realizar analisis por cada variable
var = "msf_recencydonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencydonorcont__c es 1177448. Lo que supone un 65.28976349922009% El nº de vacios para la variable msf_recencydonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2244.0 | 9884 | 1.578987 |
| 218.0 | 7264 | 1.160437 |
| 1102.0 | 7023 | 1.121937 |
| 128.0 | 6653 | 1.062829 |
| 1132.0 | 4539 | 0.725113 |
| ... | ... | ... |
| 11426.0 | 1 | 0.000160 |
| 11840.0 | 1 | 0.000160 |
| 7209.0 | 1 | 0.000160 |
| 12103.0 | 1 | 0.000160 |
| 10520.0 | 1 | 0.000160 |
10781 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_recencyrecurringdonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencyrecurringdonorcont__c es 868629. Lo que supone un 48.165678635968675% El nº de vacios para la variable msf_recencyrecurringdonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4.0 | 391646 | 41.896683 |
| 36.0 | 20951 | 2.241252 |
| 66.0 | 20310 | 2.172680 |
| 186.0 | 13427 | 1.436365 |
| 156.0 | 13005 | 1.391222 |
| 128.0 | 10337 | 1.105810 |
| 218.0 | 10233 | 1.094684 |
| 95.0 | 8499 | 0.909188 |
| 247.0 | 7675 | 0.821040 |
| 340.0 | 7139 | 0.763701 |
| 277.0 | 6996 | 0.748403 |
| 309.0 | 6101 | 0.652660 |
| 1983.0 | 5297 | 0.566651 |
| 2012.0 | 4219 | 0.451331 |
| 1648.0 | 3902 | 0.417420 |
| 2042.0 | 3888 | 0.415922 |
| 1314.0 | 3803 | 0.406829 |
| 1678.0 | 3790 | 0.405439 |
| 583.0 | 3755 | 0.401694 |
| 948.0 | 3667 | 0.392281 |
| 1955.0 | 3615 | 0.386718 |
| 1283.0 | 3555 | 0.380299 |
| 1769.0 | 3360 | 0.359439 |
| 1251.0 | 3341 | 0.357406 |
| 550.0 | 3333 | 0.356551 |
| 1740.0 | 3308 | 0.353876 |
| 1832.0 | 3256 | 0.348314 |
| 1922.0 | 3252 | 0.347886 |
| 914.0 | 3250 | 0.347672 |
| 1375.0 | 3210 | 0.343393 |
| 401.0 | 3201 | 0.342430 |
| 1405.0 | 3195 | 0.341788 |
| 1223.0 | 3187 | 0.340932 |
| 1618.0 | 3179 | 0.340076 |
| 1437.0 | 3175 | 0.339648 |
| 612.0 | 3165 | 0.338579 |
| 1802.0 | 3134 | 0.335262 |
| 858.0 | 3133 | 0.335155 |
| 2105.0 | 3127 | 0.334514 |
| 1863.0 | 3100 | 0.331625 |
| 1009.0 | 3095 | 0.331090 |
| 644.0 | 3092 | 0.330769 |
| 1468.0 | 3088 | 0.330342 |
| 766.0 | 3082 | 0.329700 |
| 368.0 | 3053 | 0.326597 |
| 2074.0 | 3053 | 0.326597 |
| 1709.0 | 3050 | 0.326276 |
| 886.0 | 3010 | 0.321997 |
| 1590.0 | 2989 | 0.319751 |
| 674.0 | 2986 | 0.319430 |
| 2378.0 | 2958 | 0.316435 |
| 1040.0 | 2954 | 0.316007 |
| 2410.0 | 2924 | 0.312798 |
| 493.0 | 2910 | 0.311300 |
| 1892.0 | 2902 | 0.310444 |
| 976.0 | 2890 | 0.309160 |
| 2136.0 | 2887 | 0.308839 |
| 1342.0 | 2887 | 0.308839 |
| 521.0 | 2851 | 0.304988 |
| 1559.0 | 2850 | 0.304881 |
| 827.0 | 2806 | 0.300174 |
| 431.0 | 2800 | 0.299533 |
| 462.0 | 2772 | 0.296537 |
| 795.0 | 2755 | 0.294719 |
| 736.0 | 2754 | 0.294612 |
| 1102.0 | 2751 | 0.294291 |
| 1528.0 | 2680 | 0.286695 |
| 704.0 | 2673 | 0.285947 |
| 2167.0 | 2618 | 0.280063 |
| 1496.0 | 2591 | 0.277175 |
| 2196.0 | 2528 | 0.270435 |
| 1069.0 | 2523 | 0.269900 |
| 1192.0 | 2511 | 0.268616 |
| 1131.0 | 2437 | 0.260700 |
| 2775.0 | 2431 | 0.260058 |
| 2469.0 | 2410 | 0.257812 |
| 2347.0 | 2408 | 0.257598 |
| 2319.0 | 2404 | 0.257170 |
| 2258.0 | 2376 | 0.254175 |
| 2228.0 | 2346 | 0.250965 |
| 1161.0 | 2302 | 0.246259 |
| 2287.0 | 2289 | 0.244868 |
| 2714.0 | 2283 | 0.244226 |
| 2439.0 | 2275 | 0.243370 |
| 3869.0 | 2256 | 0.241338 |
| 2742.0 | 2228 | 0.238342 |
| 2501.0 | 2219 | 0.237380 |
| 3109.0 | 2142 | 0.229142 |
| 3474.0 | 2139 | 0.228821 |
| 4205.0 | 2138 | 0.228714 |
| 3140.0 | 2119 | 0.226682 |
| 2532.0 | 2103 | 0.224970 |
| 2837.0 | 2095 | 0.224115 |
| 2563.0 | 2091 | 0.223687 |
| 3839.0 | 2055 | 0.219835 |
| 2654.0 | 2041 | 0.218338 |
| 2685.0 | 2040 | 0.218231 |
| 3932.0 | 2028 | 0.216947 |
| 2867.0 | 2026 | 0.216733 |
| 4175.0 | 2021 | 0.216198 |
| 2593.0 | 2016 | 0.215663 |
| 4237.0 | 2013 | 0.215342 |
| 3078.0 | 1989 | 0.212775 |
| 3809.0 | 1982 | 0.212026 |
| 3050.0 | 1980 | 0.211812 |
| 2804.0 | 1977 | 0.211491 |
| 3020.0 | 1970 | 0.210743 |
| 3442.0 | 1966 | 0.210315 |
| 2623.0 | 1932 | 0.206677 |
| 3505.0 | 1912 | 0.204538 |
| 2929.0 | 1903 | 0.203575 |
| 2958.0 | 1889 | 0.202077 |
| 3781.0 | 1834 | 0.196194 |
| 2896.0 | 1822 | 0.194910 |
| 4114.0 | 1812 | 0.193840 |
| 4146.0 | 1811 | 0.193733 |
| 3566.0 | 1791 | 0.191594 |
| 4083.0 | 1788 | 0.191273 |
| 3900.0 | 1780 | 0.190417 |
| 3414.0 | 1780 | 0.190417 |
| 3201.0 | 1767 | 0.189026 |
| 3960.0 | 1761 | 0.188385 |
| 4023.0 | 1753 | 0.187529 |
| 2987.0 | 1736 | 0.185710 |
| 3749.0 | 1710 | 0.182929 |
| 3596.0 | 1670 | 0.178650 |
| 3230.0 | 1660 | 0.177580 |
| 3993.0 | 1634 | 0.174799 |
| 3384.0 | 1633 | 0.174692 |
| 3351.0 | 1630 | 0.174371 |
| 3263.0 | 1628 | 0.174157 |
| 3293.0 | 1607 | 0.171910 |
| 3169.0 | 1603 | 0.171482 |
| 3659.0 | 1585 | 0.169557 |
| 4601.0 | 1539 | 0.164636 |
| 4569.0 | 1539 | 0.164636 |
| 4512.0 | 1533 | 0.163994 |
| 3719.0 | 1526 | 0.163245 |
| 4051.0 | 1517 | 0.162282 |
| 4266.0 | 1482 | 0.158538 |
| 3320.0 | 1475 | 0.157789 |
| 4295.0 | 1435 | 0.153510 |
| 4327.0 | 1429 | 0.152869 |
| 4540.0 | 1424 | 0.152334 |
| 3533.0 | 1422 | 0.152120 |
| 4390.0 | 1389 | 0.148590 |
| 4420.0 | 1369 | 0.146450 |
| 3627.0 | 1365 | 0.146022 |
| 4481.0 | 1352 | 0.144631 |
| 3687.0 | 1345 | 0.143883 |
| 4450.0 | 1275 | 0.136394 |
| 5268.0 | 1271 | 0.135966 |
| 5300.0 | 1247 | 0.133399 |
| 4358.0 | 1239 | 0.132543 |
| 4660.0 | 1216 | 0.130083 |
| 5332.0 | 1207 | 0.129120 |
| 5392.0 | 1188 | 0.127087 |
| 4692.0 | 1180 | 0.126232 |
| 4966.0 | 1179 | 0.126125 |
| 4631.0 | 1175 | 0.125697 |
| 5423.0 | 1155 | 0.123557 |
| 4723.0 | 1097 | 0.117353 |
| 4755.0 | 1082 | 0.115748 |
| 4814.0 | 1067 | 0.114143 |
| 5665.0 | 1066 | 0.114036 |
| 5605.0 | 1043 | 0.111576 |
| 4784.0 | 1041 | 0.111362 |
| 4846.0 | 1035 | 0.110720 |
| 5240.0 | 1033 | 0.110506 |
| 5633.0 | 1029 | 0.110078 |
| 4905.0 | 1028 | 0.109971 |
| 4877.0 | 1025 | 0.109650 |
| 5210.0 | 1024 | 0.109543 |
| 5027.0 | 1023 | 0.109436 |
| 4933.0 | 1022 | 0.109329 |
| 5697.0 | 985 | 0.105371 |
| 4996.0 | 967 | 0.103446 |
| 5482.0 | 947 | 0.101306 |
| 5360.0 | 945 | 0.101092 |
| 5057.0 | 940 | 0.100557 |
| 5545.0 | 927 | 0.099167 |
| 5573.0 | 895 | 0.095743 |
| 5178.0 | 895 | 0.095743 |
| 5514.0 | 867 | 0.092748 |
| 5941.0 | 863 | 0.092320 |
| 5147.0 | 847 | 0.090609 |
| 5119.0 | 845 | 0.090395 |
| 5756.0 | 842 | 0.090074 |
| 5727.0 | 836 | 0.089432 |
| 5848.0 | 809 | 0.086544 |
| 5787.0 | 806 | 0.086223 |
| 6029.0 | 772 | 0.082585 |
| 5972.0 | 769 | 0.082264 |
| 5087.0 | 760 | 0.081302 |
| 5447.0 | 750 | 0.080232 |
| 5997.0 | 717 | 0.076702 |
| 5909.0 | 702 | 0.075097 |
| 5819.0 | 700 | 0.074883 |
| 5877.0 | 646 | 0.069106 |
| 7738.0 | 643 | 0.068786 |
| 6123.0 | 628 | 0.067181 |
| 6364.0 | 603 | 0.064506 |
| 6393.0 | 597 | 0.063865 |
| 6062.0 | 571 | 0.061083 |
| 6151.0 | 542 | 0.057981 |
| 6426.0 | 532 | 0.056911 |
| 6336.0 | 521 | 0.055734 |
| 6245.0 | 521 | 0.055734 |
| 6305.0 | 520 | 0.055627 |
| 6214.0 | 519 | 0.055520 |
| 6091.0 | 518 | 0.055414 |
| 6274.0 | 508 | 0.054344 |
| 6184.0 | 495 | 0.052953 |
| 6487.0 | 448 | 0.047925 |
| 6669.0 | 443 | 0.047390 |
| 6518.0 | 439 | 0.046962 |
| 6700.0 | 428 | 0.045786 |
| 6549.0 | 423 | 0.045251 |
| 6728.0 | 418 | 0.044716 |
| 6456.0 | 407 | 0.043539 |
| 6639.0 | 396 | 0.042362 |
| 6759.0 | 390 | 0.041721 |
| 6609.0 | 385 | 0.041186 |
| 6882.0 | 380 | 0.040651 |
| 6578.0 | 358 | 0.038297 |
| 7097.0 | 355 | 0.037976 |
| 7068.0 | 329 | 0.035195 |
| 7128.0 | 323 | 0.034553 |
| 6946.0 | 320 | 0.034232 |
| 6849.0 | 315 | 0.033697 |
| 7037.0 | 307 | 0.032842 |
| 7493.0 | 286 | 0.030595 |
| 6820.0 | 286 | 0.030595 |
| 7462.0 | 277 | 0.029632 |
| 6789.0 | 273 | 0.029204 |
| 8315.0 | 267 | 0.028563 |
| 7007.0 | 263 | 0.028135 |
| 7585.0 | 254 | 0.027172 |
| 7950.0 | 250 | 0.026744 |
| 8192.0 | 242 | 0.025888 |
| 7403.0 | 236 | 0.025246 |
| 6976.0 | 236 | 0.025246 |
| 7159.0 | 235 | 0.025139 |
| 7858.0 | 232 | 0.024818 |
| 7220.0 | 215 | 0.023000 |
| 7312.0 | 207 | 0.022144 |
| 7615.0 | 207 | 0.022144 |
| 7250.0 | 207 | 0.022144 |
| 6915.0 | 205 | 0.021930 |
| 7434.0 | 203 | 0.021716 |
| 7189.0 | 200 | 0.021395 |
| 8042.0 | 200 | 0.021395 |
| 9776.0 | 194 | 0.020753 |
| 7342.0 | 187 | 0.020004 |
| 8133.0 | 187 | 0.020004 |
| 7646.0 | 186 | 0.019898 |
| 8223.0 | 185 | 0.019791 |
| 7373.0 | 183 | 0.019577 |
| 8164.0 | 178 | 0.019042 |
| 7281.0 | 176 | 0.018828 |
| 7524.0 | 171 | 0.018293 |
| 7889.0 | 171 | 0.018293 |
| 7554.0 | 169 | 0.018079 |
| 7827.0 | 165 | 0.017651 |
| 9684.0 | 162 | 0.017330 |
| 9411.0 | 162 | 0.017330 |
| 8254.0 | 157 | 0.016795 |
| 8494.0 | 156 | 0.016688 |
| 8407.0 | 154 | 0.016474 |
| 9288.0 | 154 | 0.016474 |
| 7768.0 | 153 | 0.016367 |
| 7980.0 | 152 | 0.016260 |
| 7919.0 | 151 | 0.016153 |
| 9653.0 | 148 | 0.015832 |
| 8923.0 | 146 | 0.015618 |
| 10141.0 | 146 | 0.015618 |
| 8558.0 | 145 | 0.015512 |
| 7799.0 | 145 | 0.015512 |
| 9594.0 | 143 | 0.015298 |
| 9959.0 | 143 | 0.015298 |
| 8954.0 | 136 | 0.014549 |
| 9868.0 | 133 | 0.014228 |
| 8345.0 | 131 | 0.014014 |
| 10019.0 | 130 | 0.013907 |
| 8072.0 | 130 | 0.013907 |
| 9503.0 | 129 | 0.013800 |
| 8773.0 | 129 | 0.013800 |
| 8468.0 | 124 | 0.013265 |
| 8864.0 | 123 | 0.013158 |
| 9319.0 | 122 | 0.013051 |
| 8011.0 | 121 | 0.012944 |
| 9229.0 | 120 | 0.012837 |
| 9046.0 | 120 | 0.012837 |
| 8376.0 | 118 | 0.012623 |
| 8103.0 | 117 | 0.012516 |
| 8284.0 | 114 | 0.012195 |
| 2198.0 | 110 | 0.011767 |
| 8437.0 | 109 | 0.011660 |
| 9260.0 | 108 | 0.011553 |
| 10049.0 | 107 | 0.011446 |
| 8577.0 | 107 | 0.011446 |
| 8681.0 | 107 | 0.011446 |
| 10384.0 | 107 | 0.011446 |
| 7668.0 | 104 | 0.011125 |
| 8620.0 | 101 | 0.010805 |
| 8521.0 | 100 | 0.010698 |
| 10234.0 | 98 | 0.010484 |
| 9138.0 | 97 | 0.010377 |
| 10325.0 | 91 | 0.009735 |
| 8895.0 | 90 | 0.009628 |
| 8985.0 | 89 | 0.009521 |
| 9625.0 | 85 | 0.009093 |
| 10414.0 | 84 | 0.008986 |
| 7665.0 | 83 | 0.008879 |
| 8803.0 | 82 | 0.008772 |
| 2379.0 | 77 | 0.008237 |
| 8711.0 | 75 | 0.008023 |
| 9990.0 | 73 | 0.007809 |
| 2045.0 | 72 | 0.007702 |
| 8742.0 | 70 | 0.007488 |
| 9715.0 | 68 | 0.007274 |
| 8650.0 | 68 | 0.007274 |
| 2014.0 | 67 | 0.007167 |
| 2776.0 | 67 | 0.007167 |
| 2990.0 | 66 | 0.007060 |
| 2348.0 | 66 | 0.007060 |
| 7695.0 | 66 | 0.007060 |
| 3079.0 | 65 | 0.006953 |
| 2259.0 | 65 | 0.006953 |
| 2075.0 | 64 | 0.006846 |
| 2320.0 | 63 | 0.006739 |
| 2624.0 | 63 | 0.006739 |
| 2898.0 | 62 | 0.006633 |
| 2471.0 | 61 | 0.006526 |
| 2959.0 | 59 | 0.006312 |
| 10111.0 | 59 | 0.006312 |
| 9533.0 | 59 | 0.006312 |
| 9076.0 | 58 | 0.006205 |
| 2806.0 | 57 | 0.006098 |
| 10356.0 | 57 | 0.006098 |
| 9168.0 | 55 | 0.005884 |
| 3051.0 | 55 | 0.005884 |
| 9805.0 | 55 | 0.005884 |
| 3416.0 | 54 | 0.005777 |
| 9745.0 | 54 | 0.005777 |
| 2289.0 | 53 | 0.005670 |
| 2745.0 | 53 | 0.005670 |
| 1741.0 | 52 | 0.005563 |
| 3141.0 | 52 | 0.005563 |
| 10502.0 | 52 | 0.005563 |
| 3110.0 | 52 | 0.005563 |
| 8834.0 | 52 | 0.005563 |
| 10264.0 | 52 | 0.005563 |
| 2440.0 | 51 | 0.005456 |
| 1924.0 | 50 | 0.005349 |
| 3232.0 | 50 | 0.005349 |
| 9107.0 | 50 | 0.005349 |
| 2106.0 | 50 | 0.005349 |
| 3506.0 | 49 | 0.005242 |
| 1680.0 | 48 | 0.005135 |
| 3597.0 | 48 | 0.005135 |
| 1833.0 | 47 | 0.005028 |
| 9015.0 | 47 | 0.005028 |
| 10081.0 | 47 | 0.005028 |
| 3355.0 | 46 | 0.004921 |
| 9441.0 | 46 | 0.004921 |
| 1315.0 | 46 | 0.004921 |
| 4085.0 | 45 | 0.004814 |
| 9380.0 | 45 | 0.004814 |
| 5270.0 | 45 | 0.004814 |
| 10507.0 | 45 | 0.004814 |
| 4115.0 | 44 | 0.004707 |
| 1253.0 | 43 | 0.004600 |
| 9472.0 | 43 | 0.004600 |
| 9896.0 | 42 | 0.004493 |
| 1710.0 | 41 | 0.004386 |
| 9350.0 | 40 | 0.004279 |
| 9564.0 | 40 | 0.004279 |
| 10596.0 | 39 | 0.004172 |
| 1224.0 | 39 | 0.004172 |
| 3536.0 | 39 | 0.004172 |
| 10166.0 | 38 | 0.004065 |
| 4024.0 | 38 | 0.004065 |
| 9929.0 | 38 | 0.004065 |
| 10476.0 | 37 | 0.003958 |
| 9199.0 | 37 | 0.003958 |
| 3202.0 | 37 | 0.003958 |
| 3171.0 | 36 | 0.003851 |
| 10749.0 | 36 | 0.003851 |
| 3294.0 | 35 | 0.003744 |
| 1284.0 | 35 | 0.003744 |
| 5393.0 | 35 | 0.003744 |
| 3962.0 | 35 | 0.003744 |
| 9836.0 | 34 | 0.003637 |
| 3385.0 | 34 | 0.003637 |
| 1163.0 | 34 | 0.003637 |
| 10443.0 | 34 | 0.003637 |
| 1132.0 | 34 | 0.003637 |
| 3324.0 | 33 | 0.003530 |
| 3567.0 | 33 | 0.003530 |
| 4451.0 | 33 | 0.003530 |
| 1529.0 | 32 | 0.003423 |
| 10694.0 | 32 | 0.003423 |
| 1193.0 | 32 | 0.003423 |
| 1345.0 | 31 | 0.003316 |
| 4602.0 | 31 | 0.003316 |
| 10774.0 | 30 | 0.003209 |
| 1406.0 | 30 | 0.003209 |
| 5242.0 | 30 | 0.003209 |
| 1771.0 | 30 | 0.003209 |
| 3444.0 | 30 | 0.003209 |
| 3720.0 | 30 | 0.003209 |
| 1894.0 | 30 | 0.003209 |
| 7703.0 | 29 | 0.003102 |
| 1376.0 | 29 | 0.003102 |
| 4206.0 | 29 | 0.003102 |
| 3840.0 | 28 | 0.002995 |
| 4571.0 | 27 | 0.002888 |
| 3901.0 | 27 | 0.002888 |
| 10964.0 | 27 | 0.002888 |
| 10294.0 | 27 | 0.002888 |
| 1649.0 | 27 | 0.002888 |
| 5211.0 | 27 | 0.002888 |
| 5301.0 | 26 | 0.002781 |
| 5058.0 | 26 | 0.002781 |
| 5089.0 | 26 | 0.002781 |
| 7700.0 | 26 | 0.002781 |
| 11051.0 | 26 | 0.002781 |
| 4816.0 | 25 | 0.002674 |
| 4298.0 | 25 | 0.002674 |
| 4267.0 | 25 | 0.002674 |
| 4359.0 | 25 | 0.002674 |
| 3871.0 | 25 | 0.002674 |
| 10869.0 | 24 | 0.002567 |
| 5515.0 | 24 | 0.002567 |
| 3750.0 | 24 | 0.002567 |
| 5576.0 | 24 | 0.002567 |
| 5636.0 | 23 | 0.002460 |
| 3689.0 | 23 | 0.002460 |
| 1071.0 | 23 | 0.002460 |
| 10203.0 | 22 | 0.002353 |
| 10960.0 | 22 | 0.002353 |
| 5028.0 | 21 | 0.002246 |
| 4632.0 | 21 | 0.002246 |
| 3475.0 | 20 | 0.002140 |
| 1498.0 | 20 | 0.002140 |
| 5362.0 | 20 | 0.002140 |
| 5485.0 | 20 | 0.002140 |
| 4054.0 | 20 | 0.002140 |
| 4328.0 | 20 | 0.002140 |
| 3628.0 | 19 | 0.002033 |
| 5607.0 | 19 | 0.002033 |
| 5728.0 | 19 | 0.002033 |
| 11359.0 | 18 | 0.001926 |
| 10142.0 | 18 | 0.001926 |
| 4785.0 | 18 | 0.001926 |
| 6428.0 | 18 | 0.001926 |
| 883.0 | 18 | 0.001926 |
| 4693.0 | 18 | 0.001926 |
| 5150.0 | 18 | 0.001926 |
| 11206.0 | 17 | 0.001819 |
| 10743.0 | 17 | 0.001819 |
| 5820.0 | 17 | 0.001819 |
| 5454.0 | 17 | 0.001819 |
| 4724.0 | 17 | 0.001819 |
| 4936.0 | 17 | 0.001819 |
| 5181.0 | 17 | 0.001819 |
| 11086.0 | 16 | 0.001712 |
| 10834.0 | 16 | 0.001712 |
| 10537.0 | 16 | 0.001712 |
| 6307.0 | 16 | 0.001712 |
| 4967.0 | 16 | 0.001712 |
| 6093.0 | 16 | 0.001712 |
| 10718.0 | 15 | 0.001605 |
| 6216.0 | 15 | 0.001605 |
| 10599.0 | 15 | 0.001605 |
| 5546.0 | 15 | 0.001605 |
| 8589.0 | 14 | 0.001498 |
| 11139.0 | 14 | 0.001498 |
| 10415.0 | 14 | 0.001498 |
| 11176.0 | 14 | 0.001498 |
| 11145.0 | 14 | 0.001498 |
| 5973.0 | 14 | 0.001498 |
| 5759.0 | 13 | 0.001391 |
| 6550.0 | 13 | 0.001391 |
| 5120.0 | 13 | 0.001391 |
| 5667.0 | 13 | 0.001391 |
| 9898.0 | 13 | 0.001391 |
| 11025.0 | 13 | 0.001391 |
| 10721.0 | 13 | 0.001391 |
| 6124.0 | 12 | 0.001284 |
| 8529.0 | 12 | 0.001284 |
| 10050.0 | 12 | 0.001284 |
| 6277.0 | 12 | 0.001284 |
| 6154.0 | 11 | 0.001177 |
| 5942.0 | 11 | 0.001177 |
| 4663.0 | 11 | 0.001177 |
| 6246.0 | 11 | 0.001177 |
| 5881.0 | 10 | 0.001070 |
| 6032.0 | 10 | 0.001070 |
| 5789.0 | 10 | 0.001070 |
| 5698.0 | 10 | 0.001070 |
| 10929.0 | 10 | 0.001070 |
| 4997.0 | 10 | 0.001070 |
| 6458.0 | 9 | 0.000963 |
| 10841.0 | 9 | 0.000963 |
| 10780.0 | 9 | 0.000963 |
| 10624.0 | 9 | 0.000963 |
| 6519.0 | 9 | 0.000963 |
| 6001.0 | 8 | 0.000856 |
| 11329.0 | 8 | 0.000856 |
| 10568.0 | 8 | 0.000856 |
| 11098.0 | 8 | 0.000856 |
| 10805.0 | 8 | 0.000856 |
| 11419.0 | 7 | 0.000749 |
| 5851.0 | 7 | 0.000749 |
| 11224.0 | 7 | 0.000749 |
| 10887.0 | 7 | 0.000749 |
| 10652.0 | 7 | 0.000749 |
| 5912.0 | 7 | 0.000749 |
| 10811.0 | 6 | 0.000642 |
| 11163.0 | 6 | 0.000642 |
| 6489.0 | 6 | 0.000642 |
| 5.0 | 6 | 0.000642 |
| 11510.0 | 6 | 0.000642 |
| 6185.0 | 6 | 0.000642 |
| 6063.0 | 6 | 0.000642 |
| 10172.0 | 5 | 0.000535 |
| 9837.0 | 5 | 0.000535 |
| 10629.0 | 5 | 0.000535 |
| 11857.0 | 5 | 0.000535 |
| 10295.0 | 5 | 0.000535 |
| 10690.0 | 4 | 0.000428 |
| 11079.0 | 4 | 0.000428 |
| 11856.0 | 4 | 0.000428 |
| 11542.0 | 4 | 0.000428 |
| 11114.0 | 4 | 0.000428 |
| 10446.0 | 4 | 0.000428 |
| 7667.0 | 4 | 0.000428 |
| 10933.0 | 3 | 0.000321 |
| 11267.0 | 3 | 0.000321 |
| 11633.0 | 3 | 0.000321 |
| 11029.0 | 3 | 0.000321 |
| 6397.0 | 3 | 0.000321 |
| 11237.0 | 3 | 0.000321 |
| 9806.0 | 3 | 0.000321 |
| 11285.0 | 3 | 0.000321 |
| 11695.0 | 3 | 0.000321 |
| 37.0 | 3 | 0.000321 |
| 11826.0 | 3 | 0.000321 |
| 11298.0 | 2 | 0.000214 |
| 248.0 | 2 | 0.000214 |
| 11479.0 | 2 | 0.000214 |
| 11567.0 | 2 | 0.000214 |
| 11055.0 | 2 | 0.000214 |
| 11076.0 | 2 | 0.000214 |
| 10660.0 | 2 | 0.000214 |
| 463.0 | 2 | 0.000214 |
| 796.0 | 2 | 0.000214 |
| 11664.0 | 2 | 0.000214 |
| 11358.0 | 2 | 0.000214 |
| 11238.0 | 2 | 0.000214 |
| 7690.0 | 2 | 0.000214 |
| 11772.0 | 2 | 0.000214 |
| 11103.0 | 2 | 0.000214 |
| 10902.0 | 2 | 0.000214 |
| 11370.0 | 2 | 0.000214 |
| 11572.0 | 1 | 0.000107 |
| 8698.0 | 1 | 0.000107 |
| 11603.0 | 1 | 0.000107 |
| 9561.0 | 1 | 0.000107 |
| 11476.0 | 1 | 0.000107 |
| 11867.0 | 1 | 0.000107 |
| 11723.0 | 1 | 0.000107 |
| 10572.0 | 1 | 0.000107 |
| 11631.0 | 1 | 0.000107 |
| 767.0 | 1 | 0.000107 |
| 11888.0 | 1 | 0.000107 |
| 11968.0 | 1 | 0.000107 |
| 705.0 | 1 | 0.000107 |
| 11450.0 | 1 | 0.000107 |
| 11937.0 | 1 | 0.000107 |
| 11873.0 | 1 | 0.000107 |
| 8731.0 | 1 | 0.000107 |
| 11420.0 | 1 | 0.000107 |
| 1734.0 | 1 | 0.000107 |
| 11793.0 | 1 | 0.000107 |
| 10994.0 | 1 | 0.000107 |
| 11037.0 | 1 | 0.000107 |
| 11022.0 | 1 | 0.000107 |
| 219.0 | 1 | 0.000107 |
| 11053.0 | 1 | 0.000107 |
| 11026.0 | 1 | 0.000107 |
| 310.0 | 1 | 0.000107 |
| 67.0 | 1 | 0.000107 |
| 949.0 | 1 | 0.000107 |
| 7697.0 | 1 | 0.000107 |
| 198.0 | 1 | 0.000107 |
| 1006.0 | 1 | 0.000107 |
| 432.0 | 1 | 0.000107 |
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencytotalcont__c es 507444. Lo que supone un 28.13788698023033% El nº de vacios para la variable msf_recencytotalcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4.0 | 393331 | 30.350200 |
| 36.0 | 21608 | 1.667316 |
| 66.0 | 20879 | 1.611065 |
| 186.0 | 13454 | 1.038137 |
| 156.0 | 12853 | 0.991763 |
| ... | ... | ... |
| 4486.0 | 1 | 0.000077 |
| 4479.0 | 1 | 0.000077 |
| 10268.0 | 1 | 0.000077 |
| 11742.0 | 1 | 0.000077 |
| 3421.0 | 1 | 0.000077 |
10599 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_percomssummary__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_percomssummary__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_percomssummary__c es 1. Lo que supone un 5.545023092248668e-05%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Todo | 1205184 | 66.827731 |
| Varios | 314074 | 17.415476 |
| Nada | 203118 | 11.262940 |
| No captación de fondos | 80975 | 4.490082 |
| Sólo certificado fiscal | 67 | 0.003715 |
| 1 | 0.000055 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1177448 | 65.289763 |
| 1.0 | 85687 | 4.751364 |
| 1.5 | 57575 | 3.192547 |
| 1.8 | 57371 | 3.181235 |
| 1.6 | 53844 | 2.985662 |
| 1.2 | 50582 | 2.804784 |
| 1.4 | 46243 | 2.564185 |
| 1.7 | 23383 | 1.296593 |
| 1.9 | 20393 | 1.130797 |
| 2.5 | 18950 | 1.050782 |
| 2.0 | 18407 | 1.020672 |
| 3.0 | 18242 | 1.011523 |
| 2.3 | 18030 | 0.999768 |
| 2.1 | 15769 | 0.874395 |
| 2.2 | 13167 | 0.730113 |
| 2.8 | 11864 | 0.657862 |
| 2.4 | 9239 | 0.512305 |
| 3.5 | 9205 | 0.510419 |
| 3.3 | 9097 | 0.504431 |
| 3.2 | 8156 | 0.452252 |
| 2.6 | 7530 | 0.417540 |
| 3.8 | 7305 | 0.405064 |
| 2.7 | 6664 | 0.369520 |
| 3.6 | 5507 | 0.305364 |
| 4.1 | 4687 | 0.259895 |
| 3.4 | 4637 | 0.257123 |
| 3.7 | 4495 | 0.249249 |
| 4.0 | 4386 | 0.243205 |
| 2.9 | 4207 | 0.233279 |
| 3.9 | 3776 | 0.209380 |
| 3.1 | 3762 | 0.208604 |
| 1.3 | 3613 | 0.200342 |
| 4.2 | 2944 | 0.163245 |
| 4.4 | 2588 | 0.143505 |
| 4.3 | 2578 | 0.142951 |
| 4.5 | 2033 | 0.112730 |
| 4.6 | 1599 | 0.088665 |
| 4.8 | 1420 | 0.078739 |
| 4.7 | 1276 | 0.070754 |
| 5.0 | 1170 | 0.064877 |
| 4.9 | 887 | 0.049184 |
| 5.1 | 711 | 0.039425 |
| 5.2 | 446 | 0.024731 |
| 5.5 | 354 | 0.019629 |
| 5.4 | 343 | 0.019019 |
| 5.3 | 310 | 0.017190 |
| 0.2 | 224 | 0.012421 |
| 5.7 | 183 | 0.010147 |
| 5.6 | 181 | 0.010036 |
| 6.0 | 178 | 0.009870 |
| 5.8 | 93 | 0.005157 |
| 5.9 | 87 | 0.004824 |
| 0.4 | 86 | 0.004769 |
| 6.2 | 66 | 0.003660 |
| 0.6 | 65 | 0.003604 |
| 6.4 | 65 | 0.003604 |
| 0.8 | 58 | 0.003216 |
| 6.5 | 58 | 0.003216 |
| 6.1 | 57 | 0.003161 |
| 0.5 | 40 | 0.002218 |
| 6.6 | 22 | 0.001220 |
| 0.7 | 21 | 0.001164 |
| 7.0 | 20 | 0.001109 |
| 6.3 | 11 | 0.000610 |
| 0.9 | 9 | 0.000499 |
| 6.8 | 7 | 0.000388 |
| 6.7 | 5 | 0.000277 |
| 1.1 | 3 | 0.000166 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 867770 | 48.118047 |
| 5.0 | 131141 | 7.271799 |
| 4.5 | 97173 | 5.388265 |
| 3.5 | 76494 | 4.241610 |
| 0.4 | 57835 | 3.206964 |
| 0.2 | 43619 | 2.418684 |
| 3.0 | 38256 | 2.121304 |
| 0.6 | 32846 | 1.821318 |
| 1.9 | 29234 | 1.621032 |
| 2.1 | 28489 | 1.579722 |
| 1.7 | 27945 | 1.549557 |
| 1.0 | 27282 | 1.512793 |
| 0.8 | 25872 | 1.434608 |
| 0.5 | 21279 | 1.179925 |
| 0.7 | 21045 | 1.166950 |
| 2.0 | 20994 | 1.164122 |
| 4.7 | 20535 | 1.138670 |
| 4.2 | 18389 | 1.019674 |
| 1.4 | 18145 | 1.006144 |
| 1.5 | 15185 | 0.842012 |
| 1.8 | 14527 | 0.805526 |
| 0.9 | 14394 | 0.798151 |
| 2.5 | 13841 | 0.767487 |
| 1.6 | 13796 | 0.764991 |
| 3.2 | 13232 | 0.733717 |
| 1.1 | 13128 | 0.727951 |
| 2.3 | 11952 | 0.662741 |
| 5.5 | 11441 | 0.634406 |
| 4.0 | 11273 | 0.625090 |
| 1.2 | 11248 | 0.623704 |
| 1.3 | 9431 | 0.522951 |
| 4.4 | 9403 | 0.521399 |
| 3.9 | 8215 | 0.455524 |
| 2.9 | 5830 | 0.323275 |
| 2.7 | 5153 | 0.285735 |
| 2.2 | 4636 | 0.257067 |
| 2.4 | 2281 | 0.126482 |
| 6.0 | 2103 | 0.116612 |
| 3.7 | 1697 | 0.094099 |
| 3.6 | 1357 | 0.075246 |
| 4.1 | 1343 | 0.074470 |
| 2.6 | 1061 | 0.058833 |
| 3.4 | 753 | 0.041754 |
| 5.2 | 752 | 0.041699 |
| 4.9 | 371 | 0.020572 |
| 3.1 | 146 | 0.008096 |
| 5.7 | 143 | 0.007929 |
| 6.5 | 92 | 0.005101 |
| 4.6 | 75 | 0.004159 |
| 5.4 | 67 | 0.003715 |
| 2.8 | 45 | 0.002495 |
| 3.3 | 31 | 0.001719 |
| 4.3 | 25 | 0.001386 |
| 3.8 | 18 | 0.000998 |
| 5.1 | 12 | 0.000665 |
| 6.2 | 6 | 0.000333 |
| 4.8 | 4 | 0.000222 |
| 5.9 | 3 | 0.000166 |
| 7.0 | 2 | 0.000111 |
| 5.6 | 2 | 0.000111 |
| 6.1 | 1 | 0.000055 |
| 6.7 | 1 | 0.000055 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrvtotal__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 506626 | 28.092529 |
| 5.0 | 204141 | 11.319666 |
| 4.2 | 189206 | 10.491516 |
| 1.8 | 155567 | 8.626226 |
| 2.6 | 123064 | 6.823927 |
| 1.0 | 94838 | 5.258789 |
| 3.4 | 70635 | 3.916727 |
| 1.2 | 62393 | 3.459706 |
| 1.6 | 54394 | 3.016160 |
| 1.4 | 46581 | 2.582927 |
| 2.0 | 43373 | 2.405043 |
| 3.6 | 34596 | 1.918356 |
| 2.2 | 31964 | 1.772411 |
| 4.4 | 30610 | 1.697332 |
| 4.6 | 28814 | 1.597743 |
| 3.8 | 28472 | 1.578779 |
| 5.8 | 22880 | 1.268701 |
| 2.4 | 11962 | 0.663296 |
| 4.8 | 11208 | 0.621486 |
| 2.8 | 11205 | 0.621320 |
| 4.0 | 9431 | 0.522951 |
| 3.0 | 8282 | 0.459239 |
| 6.6 | 6257 | 0.346952 |
| 3.2 | 4101 | 0.227401 |
| 5.2 | 3544 | 0.196516 |
| 5.4 | 2623 | 0.145446 |
| 6.0 | 1654 | 0.091715 |
| 5.6 | 1282 | 0.071087 |
| 6.2 | 1042 | 0.057779 |
| 7.4 | 860 | 0.047687 |
| 6.4 | 527 | 0.029222 |
| 6.8 | 326 | 0.018077 |
| 7.0 | 161 | 0.008927 |
| 7.6 | 151 | 0.008373 |
| 8.2 | 137 | 0.007597 |
| 0.8 | 122 | 0.006765 |
| 7.2 | 109 | 0.006044 |
| 7.8 | 87 | 0.004824 |
| 0.6 | 55 | 0.003050 |
| 0.2 | 49 | 0.002717 |
| 0.4 | 47 | 0.002606 |
| 8.0 | 43 | 0.002384 |
# Vamos a realizar analisis por cada variable
var = "msf_mailingsegment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_mailingsegment__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_mailingsegment__c es 8. Lo que supone un 0.0004436018473798934%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| No se está calculando la cadencia de donante | 431891 | 23.948456 |
| SOC NO REC SIN EXTRA | 313671 | 17.393129 |
| BAJAS ANTIGUAS | 154299 | 8.555915 |
| DON MUY ANTIGUOS | 150956 | 8.370545 |
| BAJAS MUY ANTIGUAS | 134593 | 7.463213 |
| BAJAS NO REC | 126262 | 7.001257 |
| BAJAS ACT | 50271 | 2.787539 |
| DON ANTIGUOS | 48193 | 2.672313 |
| SOC CON EXTRA ACT | 47970 | 2.659948 |
| BAJAS REC | 41715 | 2.313106 |
| DON UNICO REC | 40457 | 2.243350 |
| SOC CON EXTRA NO REC | 38684 | 2.145037 |
| EMPRESAS NO SOCIAS | 36971 | 2.050050 |
| DON 1R AÑO | 36068 | 1.999979 |
| SOC NUEVOS | 29424 | 1.631568 |
| SOC CON EXTRA REC | 28789 | 1.596357 |
| SOC REC SIN EXTRA | 21233 | 1.177375 |
| DON UNICO NO REC | 20670 | 1.146156 |
| DON PS ACT | 12401 | 0.687638 |
| DON OCA ACT | 9834 | 0.545298 |
| DON OCA REC | 9354 | 0.518681 |
| DON OCA NO REC | 9196 | 0.509920 |
| DON PS NO REC | 4809 | 0.266660 |
| DON PS REC | 2691 | 0.149217 |
| EMPRESAS SOCIAS | 2381 | 0.132027 |
| No cumple ninguno de los criterios anteriores | 628 | 0.034823 |
| 8 | 0.000444 |
# Vamos a realizar analisis por cada variable
var = "msf_membertype__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_membertype__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_membertype__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Baja | 425726 | 23.606605 |
| Nada | 413729 | 22.941369 |
| Socio | 301335 | 16.709095 |
| Exdonante | 300207 | 16.646547 |
| Socio + Exdonante | 132714 | 7.359022 |
| Baja + Exdonante | 79431 | 4.404467 |
| Donante | 59189 | 3.282044 |
| Socio + Donante | 48175 | 2.671315 |
| Nada (Donante SMS) | 36990 | 2.051104 |
| Baja + Donante | 5923 | 0.328432 |
# Vamos a realizar analisis por cada variable
var = "npo02__totaloppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__totaloppamount__c es 1. Lo que supone un 5.545023092248668e-05% El nº de vacios para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.00 | 506559 | 28.088829 |
| 1.00 | 47263 | 2.620746 |
| 10.00 | 29492 | 1.635339 |
| 30.00 | 26316 | 1.459229 |
| 20.00 | 24710 | 1.370176 |
| ... | ... | ... |
| 8584.23 | 1 | 0.000055 |
| 1277.43 | 1 | 0.000055 |
| 3447.32 | 1 | 0.000055 |
| 467.54 | 1 | 0.000055 |
| 1628.70 | 1 | 0.000055 |
100601 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountthisyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1803419 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__oppamount2yearsago__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1803419 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountlastyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1803419 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year_total__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year_total__c es 507444. Lo que supone un 28.13788698023033% El nº de vacios para la variable npo02__best_gift_year_total__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.00 | 119005 | 9.182662 |
| 60.00 | 69156 | 5.336214 |
| 180.00 | 58640 | 4.524779 |
| 1.00 | 49590 | 3.826463 |
| 240.00 | 39480 | 3.046355 |
| ... | ... | ... |
| 13095.68 | 1 | 0.000077 |
| 528.49 | 1 | 0.000077 |
| 275.25 | 1 | 0.000077 |
| 254.08 | 1 | 0.000077 |
| 161.01 | 1 | 0.000077 |
15457 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_totalfiscaloppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_totalfiscaloppamount__c es 3. Lo que supone un 0.00016635069276746005% El nº de vacios para la variable msf_totalfiscaloppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.00 | 506572 | 28.089581 |
| 1.00 | 47198 | 2.617144 |
| 10.00 | 29567 | 1.639500 |
| 30.00 | 26334 | 1.460229 |
| 20.00 | 24746 | 1.372174 |
| ... | ... | ... |
| 3220.59 | 1 | 0.000055 |
| 1225.69 | 1 | 0.000055 |
| 2802.82 | 1 | 0.000055 |
| 12141.44 | 1 | 0.000055 |
| 1628.70 | 1 | 0.000055 |
100336 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastannualizedquota__c es 850655. Lo que supone un 47.1690161853679% El nº de vacios para la variable msf_lastannualizedquota__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 205240 | 21.541536 |
| 1.800000e+02 | 103733 | 10.887586 |
| 6.000000e+01 | 96713 | 10.150782 |
| 2.400000e+02 | 66814 | 7.012650 |
| 1.440000e+02 | 51175 | 5.371215 |
| 7.200000e+01 | 35993 | 3.777746 |
| 3.600000e+02 | 25096 | 2.634021 |
| 3.000000e+02 | 24793 | 2.602218 |
| 3.600000e+01 | 22838 | 2.397026 |
| 9.600000e+01 | 20117 | 2.111436 |
| 8.400000e+01 | 16966 | 1.780714 |
| 1.680000e+02 | 16565 | 1.738626 |
| 1.000000e+02 | 14049 | 1.474552 |
| 7.212000e+01 | 11666 | 1.224438 |
| 5.000000e+01 | 9134 | 0.958684 |
| 2.040000e+02 | 8534 | 0.895710 |
| 4.000000e+01 | 8400 | 0.881645 |
| 8.000000e+01 | 7842 | 0.823079 |
| 6.000000e+02 | 7823 | 0.821085 |
| 2.000000e+02 | 7589 | 0.796525 |
| 2.000000e+01 | 7526 | 0.789912 |
| 4.800000e+02 | 6982 | 0.732815 |
| 2.160000e+02 | 6854 | 0.719381 |
| 3.000000e+01 | 6570 | 0.689573 |
| 1.320000e+02 | 6168 | 0.647380 |
| 1.560000e+02 | 6143 | 0.644756 |
| 1.500000e+02 | 5878 | 0.616942 |
| 1.080000e+02 | 5724 | 0.600778 |
| 1.920000e+02 | 5646 | 0.592592 |
| 4.800000e+01 | 5377 | 0.564358 |
| 4.200000e+02 | 5289 | 0.555122 |
| 3.120000e+02 | 4814 | 0.505267 |
| 2.640000e+02 | 4417 | 0.463599 |
| 1.200000e+01 | 4115 | 0.431901 |
| 5.196000e+01 | 3794 | 0.398210 |
| 6.010000e+01 | 3638 | 0.381836 |
| 1.202000e+02 | 3425 | 0.359480 |
| 2.280000e+02 | 3416 | 0.358536 |
| 1.000000e+01 | 3402 | 0.357066 |
| 1.600000e+02 | 3319 | 0.348355 |
| 7.200000e+02 | 3191 | 0.334920 |
| 3.005000e+01 | 3140 | 0.329567 |
| 2.760000e+02 | 2810 | 0.294931 |
| 9.000000e+01 | 2692 | 0.282546 |
| 1.442400e+02 | 2618 | 0.274779 |
| 1.500000e+01 | 2532 | 0.265753 |
| 1.400000e+02 | 2396 | 0.251479 |
| 2.163600e+02 | 2215 | 0.232481 |
| 7.000000e+01 | 2195 | 0.230382 |
| 3.606000e+02 | 2183 | 0.229123 |
| 4.000000e+02 | 1865 | 0.195746 |
| 3.840000e+02 | 1834 | 0.192493 |
| 1.200000e+03 | 1833 | 0.192388 |
| 2.500000e+01 | 1709 | 0.179373 |
| 5.400000e+02 | 1644 | 0.172551 |
| 2.880000e+02 | 1527 | 0.160271 |
| 7.500000e+01 | 1462 | 0.153448 |
| 2.400000e+01 | 1415 | 0.148515 |
| 2.520000e+02 | 1318 | 0.138334 |
| 3.240000e+02 | 1265 | 0.132772 |
| 2.500000e+02 | 1243 | 0.130463 |
| 3.360000e+02 | 1227 | 0.128783 |
| 3.000000e+00 | 1158 | 0.121541 |
| 1.803000e+01 | 1102 | 0.115663 |
| 2.600000e+02 | 1070 | 0.112305 |
| 9.015000e+01 | 1023 | 0.107372 |
| 2.404000e+02 | 1008 | 0.105797 |
| 3.960000e+02 | 827 | 0.086800 |
| 5.000000e+00 | 812 | 0.085226 |
| 1.300000e+02 | 770 | 0.080817 |
| 5.000000e+02 | 757 | 0.079453 |
| 1.100000e+02 | 752 | 0.078928 |
| 2.800000e+02 | 737 | 0.077354 |
| 2.200000e+02 | 676 | 0.070951 |
| 1.250000e+02 | 657 | 0.068957 |
| 3.500000e+01 | 646 | 0.067803 |
| 8.400000e+02 | 641 | 0.067278 |
| 6.600000e+02 | 641 | 0.067278 |
| 3.200000e+02 | 604 | 0.063395 |
| 4.500000e+01 | 599 | 0.062870 |
| 1.800000e+01 | 516 | 0.054158 |
| 4.808000e+01 | 492 | 0.051639 |
| 7.212000e+02 | 487 | 0.051114 |
| 0.000000e+00 | 465 | 0.048805 |
| 6.500000e+01 | 458 | 0.048071 |
| 4.080000e+02 | 449 | 0.047126 |
| 9.000000e+02 | 445 | 0.046706 |
| 8.800000e+01 | 444 | 0.046601 |
| 4.320000e+02 | 443 | 0.046496 |
| 9.600000e+02 | 436 | 0.045762 |
| 1.700000e+02 | 431 | 0.045237 |
| 3.200000e+01 | 425 | 0.044607 |
| 4.200000e+01 | 397 | 0.041668 |
| 1.502500e+02 | 395 | 0.041458 |
| 2.800000e+01 | 385 | 0.040409 |
| 2.100000e+02 | 373 | 0.039149 |
| 1.000000e+03 | 367 | 0.038520 |
| 5.500000e+01 | 366 | 0.038415 |
| 7.800000e+02 | 361 | 0.037890 |
| 5.200000e+01 | 359 | 0.037680 |
| 5.600000e+01 | 341 | 0.035791 |
| 4.440000e+02 | 340 | 0.035686 |
| 3.500000e+02 | 339 | 0.035581 |
| 2.404000e+01 | 336 | 0.035266 |
| 3.720000e+02 | 328 | 0.034426 |
| 6.240000e+02 | 324 | 0.034006 |
| 5.040000e+02 | 319 | 0.033482 |
| 1.750000e+02 | 316 | 0.033167 |
| 8.000000e+02 | 294 | 0.030858 |
| 3.606000e+01 | 289 | 0.030333 |
| 1.080000e+03 | 282 | 0.029598 |
| 8.500000e+01 | 274 | 0.028758 |
| 1.650000e+02 | 274 | 0.028758 |
| 1.120000e+02 | 268 | 0.028129 |
| 1.081200e+02 | 257 | 0.026974 |
| 2.200000e+01 | 253 | 0.026554 |
| 2.300000e+02 | 245 | 0.025715 |
| 6.000000e+00 | 244 | 0.025610 |
| 3.480000e+02 | 241 | 0.025295 |
| 1.800000e+03 | 239 | 0.025085 |
| 4.560000e+02 | 236 | 0.024770 |
| 5.200000e+02 | 231 | 0.024245 |
| 9.200000e+01 | 231 | 0.024245 |
| 1.040400e+02 | 195 | 0.020467 |
| 1.802400e+02 | 192 | 0.020152 |
| 2.884800e+02 | 192 | 0.020152 |
| 1.050000e+02 | 189 | 0.019837 |
| 6.800000e+01 | 189 | 0.019837 |
| 1.040000e+02 | 174 | 0.018263 |
| 1.520000e+02 | 170 | 0.017843 |
| 6.400000e+01 | 170 | 0.017843 |
| 1.600000e+01 | 167 | 0.017528 |
| 3.485000e+01 | 165 | 0.017318 |
| 1.400000e+01 | 165 | 0.017318 |
| 2.400000e+03 | 163 | 0.017108 |
| 1.280000e+02 | 162 | 0.017003 |
| 9.616000e+01 | 162 | 0.017003 |
| 5.160000e+02 | 157 | 0.016478 |
| 3.400000e+02 | 156 | 0.016373 |
| 4.400000e+02 | 155 | 0.016268 |
| 1.803000e+02 | 152 | 0.015954 |
| 1.350000e+02 | 151 | 0.015849 |
| 1.202000e+01 | 145 | 0.015219 |
| 3.486000e+01 | 143 | 0.015009 |
| 1.730400e+02 | 142 | 0.014904 |
| 1.240000e+02 | 141 | 0.014799 |
| 1.900000e+02 | 140 | 0.014694 |
| 5.280000e+02 | 140 | 0.014694 |
| 5.400000e+01 | 140 | 0.014694 |
| 5.520000e+02 | 139 | 0.014589 |
| 2.240000e+02 | 138 | 0.014484 |
| 1.394000e+02 | 137 | 0.014379 |
| 8.640000e+02 | 135 | 0.014169 |
| 1.440000e+03 | 135 | 0.014169 |
| 1.150000e+02 | 134 | 0.014064 |
| 6.200000e+01 | 134 | 0.014064 |
| 2.250000e+02 | 133 | 0.013959 |
| 6.010000e+00 | 130 | 0.013645 |
| 1.700000e+01 | 124 | 0.013015 |
| 4.400000e+01 | 124 | 0.013015 |
| 4.500000e+02 | 124 | 0.013015 |
| 1.039200e+02 | 122 | 0.012805 |
| 1.500000e+03 | 116 | 0.012175 |
| 1.480000e+02 | 112 | 0.011755 |
| 1.394400e+02 | 111 | 0.011650 |
| 7.224000e+01 | 110 | 0.011545 |
| 2.700000e+02 | 110 | 0.011545 |
| 7.000000e+02 | 110 | 0.011545 |
| 9.500000e+01 | 109 | 0.011440 |
| 8.000000e+00 | 109 | 0.011440 |
| 3.005000e+02 | 108 | 0.011335 |
| 2.320000e+02 | 108 | 0.011335 |
| 6.600000e+01 | 102 | 0.010706 |
| 1.082400e+02 | 102 | 0.010706 |
| 3.800000e+01 | 101 | 0.010601 |
| 4.920000e+02 | 98 | 0.010286 |
| 4.680000e+02 | 97 | 0.010181 |
| 3.612000e+01 | 94 | 0.009866 |
| 2.100000e+01 | 92 | 0.009656 |
| 7.600000e+01 | 91 | 0.009551 |
| 3.800000e+02 | 90 | 0.009446 |
| 1.020000e+03 | 89 | 0.009341 |
| 4.600000e+02 | 87 | 0.009131 |
| 1.360000e+02 | 82 | 0.008607 |
| 1.550000e+02 | 81 | 0.008502 |
| 4.327200e+02 | 80 | 0.008397 |
| 1.840000e+02 | 80 | 0.008397 |
| 5.760000e+02 | 77 | 0.008082 |
| 1.640000e+02 | 76 | 0.007977 |
| 3.604000e+01 | 75 | 0.007872 |
| 1.803600e+02 | 73 | 0.007662 |
| 3.100000e+02 | 73 | 0.007662 |
| 3.300000e+02 | 71 | 0.007452 |
| 3.900000e+02 | 67 | 0.007032 |
| 5.768000e+01 | 67 | 0.007032 |
| 1.081800e+03 | 66 | 0.006927 |
| 2.000000e+03 | 66 | 0.006927 |
| 7.400000e+01 | 65 | 0.006822 |
| 2.600000e+01 | 64 | 0.006717 |
| 9.315000e+01 | 64 | 0.006717 |
| 4.207000e+01 | 63 | 0.006612 |
| 2.750000e+02 | 63 | 0.006612 |
| 1.320000e+03 | 62 | 0.006507 |
| 3.300000e+01 | 59 | 0.006193 |
| 5.600000e+02 | 58 | 0.006088 |
| 1.923200e+02 | 57 | 0.005983 |
| 1.020000e+02 | 57 | 0.005983 |
| 4.808000e+02 | 56 | 0.005878 |
| 7.800000e+01 | 56 | 0.005878 |
| 6.010000e+02 | 55 | 0.005773 |
| 1.260000e+02 | 55 | 0.005773 |
| 2.080000e+02 | 55 | 0.005773 |
| 6.300000e+01 | 54 | 0.005668 |
| 2.480000e+02 | 54 | 0.005668 |
| 6.400000e+02 | 52 | 0.005458 |
| 4.183200e+02 | 52 | 0.005458 |
| 3.600000e+03 | 51 | 0.005353 |
| 1.450000e+02 | 51 | 0.005353 |
| 5.500000e+02 | 51 | 0.005353 |
| 2.700000e+01 | 51 | 0.005353 |
| 6.396000e+01 | 51 | 0.005353 |
| 6.010100e+02 | 51 | 0.005353 |
| 2.120000e+02 | 49 | 0.005143 |
| 3.400000e+01 | 49 | 0.005143 |
| 3.000000e+03 | 48 | 0.005038 |
| 7.440000e+02 | 48 | 0.005038 |
| 6.480000e+02 | 47 | 0.004933 |
| 1.682800e+02 | 47 | 0.004933 |
| 5.640000e+02 | 47 | 0.004933 |
| 1.160000e+02 | 46 | 0.004828 |
| 1.850000e+02 | 45 | 0.004723 |
| 6.200000e+02 | 44 | 0.004618 |
| 9.316000e+01 | 44 | 0.004618 |
| 7.596000e+01 | 44 | 0.004618 |
| 5.769600e+02 | 44 | 0.004618 |
| 8.414000e+01 | 43 | 0.004513 |
| 1.720000e+02 | 43 | 0.004513 |
| 6.008000e+01 | 42 | 0.004408 |
| 5.048400e+02 | 41 | 0.004303 |
| 7.000000e+00 | 41 | 0.004303 |
| 8.200000e+01 | 40 | 0.004198 |
| 1.300000e+01 | 39 | 0.004093 |
| 3.640000e+02 | 39 | 0.004093 |
| 3.700000e+01 | 38 | 0.003988 |
| 2.900000e+02 | 38 | 0.003988 |
| 8.654400e+02 | 37 | 0.003883 |
| 1.620000e+02 | 37 | 0.003883 |
| 1.960000e+02 | 37 | 0.003883 |
| 6.360000e+02 | 36 | 0.003778 |
| 5.409000e+01 | 35 | 0.003674 |
| 7.920000e+02 | 35 | 0.003674 |
| 1.880000e+02 | 35 | 0.003674 |
| 6.120000e+02 | 35 | 0.003674 |
| 3.250000e+02 | 34 | 0.003569 |
| 3.462000e+02 | 33 | 0.003464 |
| 4.000000e+00 | 33 | 0.003464 |
| 3.750000e+02 | 33 | 0.003464 |
| 6.500000e+02 | 32 | 0.003359 |
| 3.005100e+02 | 31 | 0.003254 |
| 4.182000e+02 | 31 | 0.003254 |
| 1.560000e+03 | 31 | 0.003254 |
| 1.081800e+02 | 31 | 0.003254 |
| 1.760000e+02 | 31 | 0.003254 |
| 3.900000e+01 | 31 | 0.003254 |
| 1.442400e+03 | 30 | 0.003149 |
| 1.802800e+02 | 30 | 0.003149 |
| 4.600000e+01 | 29 | 0.003044 |
| 1.008000e+03 | 29 | 0.003044 |
| 8.412000e+01 | 28 | 0.002939 |
| 9.000000e+00 | 27 | 0.002834 |
| 2.720000e+02 | 27 | 0.002834 |
| 3.700000e+02 | 27 | 0.002834 |
| 9.800000e+01 | 27 | 0.002834 |
| 1.154000e+02 | 25 | 0.002624 |
| 1.100000e+01 | 25 | 0.002624 |
| 2.360000e+02 | 25 | 0.002624 |
| 3.650000e+02 | 25 | 0.002624 |
| 3.608000e+01 | 24 | 0.002519 |
| 6.000000e+03 | 24 | 0.002519 |
| 6.700000e+01 | 24 | 0.002519 |
| 9.400000e+01 | 24 | 0.002519 |
| 1.740000e+02 | 23 | 0.002414 |
| 1.442000e+01 | 23 | 0.002414 |
| 2.885000e+01 | 23 | 0.002414 |
| 3.920000e+02 | 22 | 0.002309 |
| 2.350000e+02 | 22 | 0.002309 |
| 2.884000e+01 | 22 | 0.002309 |
| 3.614400e+02 | 22 | 0.002309 |
| 7.700000e+01 | 21 | 0.002204 |
| 2.150000e+02 | 21 | 0.002204 |
| 3.100000e+01 | 20 | 0.002099 |
| 9.360000e+02 | 20 | 0.002099 |
| 1.600000e+03 | 20 | 0.002099 |
| 7.813000e+01 | 20 | 0.002099 |
| 2.300000e+01 | 19 | 0.001994 |
| 7.500000e+02 | 19 | 0.001994 |
| 1.140000e+03 | 19 | 0.001994 |
| 1.803000e+03 | 19 | 0.001994 |
| 7.600000e+02 | 19 | 0.001994 |
| 8.600000e+01 | 19 | 0.001994 |
| 2.523600e+02 | 19 | 0.001994 |
| 6.100000e+01 | 19 | 0.001994 |
| 8.460000e+01 | 18 | 0.001889 |
| 6.720000e+02 | 18 | 0.001889 |
| 6.800000e+02 | 18 | 0.001889 |
| 1.153600e+02 | 17 | 0.001784 |
| 2.440000e+02 | 17 | 0.001784 |
| 5.100000e+01 | 17 | 0.001784 |
| 2.050000e+02 | 16 | 0.001679 |
| 1.140000e+02 | 16 | 0.001679 |
| 7.560000e+02 | 16 | 0.001679 |
| 8.700000e+01 | 16 | 0.001679 |
| 4.250000e+02 | 16 | 0.001679 |
| 6.840000e+02 | 15 | 0.001574 |
| 6.012000e+01 | 15 | 0.001574 |
| 1.152000e+03 | 15 | 0.001574 |
| 3.040000e+02 | 15 | 0.001574 |
| 1.680000e+03 | 15 | 0.001574 |
| 9.012000e+01 | 15 | 0.001574 |
| 1.220000e+02 | 15 | 0.001574 |
| 1.260000e+03 | 15 | 0.001574 |
| 5.300000e+01 | 14 | 0.001469 |
| 5.770000e+01 | 14 | 0.001469 |
| 1.980000e+02 | 14 | 0.001469 |
| 6.960000e+02 | 14 | 0.001469 |
| 5.769000e+01 | 14 | 0.001469 |
| 1.380000e+02 | 14 | 0.001469 |
| 2.103500e+02 | 14 | 0.001469 |
| 7.300000e+01 | 14 | 0.001469 |
| 8.652000e+01 | 14 | 0.001469 |
| 6.024000e+01 | 13 | 0.001364 |
| 7.320000e+02 | 13 | 0.001364 |
| 4.100000e+02 | 13 | 0.001364 |
| 1.060000e+02 | 13 | 0.001364 |
| 2.920000e+02 | 13 | 0.001364 |
| 1.950000e+02 | 13 | 0.001364 |
| 5.700000e+01 | 13 | 0.001364 |
| 1.204800e+02 | 13 | 0.001364 |
| 1.032000e+03 | 12 | 0.001259 |
| 1.201200e+02 | 12 | 0.001259 |
| 3.726000e+02 | 12 | 0.001259 |
| 4.300000e+01 | 12 | 0.001259 |
| 1.200000e+04 | 12 | 0.001259 |
| 7.680000e+02 | 12 | 0.001259 |
| 5.800000e+02 | 12 | 0.001259 |
| 1.340000e+02 | 12 | 0.001259 |
| 2.680000e+02 | 12 | 0.001259 |
| 9.300000e+01 | 12 | 0.001259 |
| 1.540000e+02 | 11 | 0.001155 |
| 8.040000e+02 | 11 | 0.001155 |
| 5.800000e+01 | 11 | 0.001155 |
| 2.524800e+02 | 11 | 0.001155 |
| 4.100000e+01 | 11 | 0.001155 |
| 2.840000e+02 | 11 | 0.001155 |
| 3.440000e+02 | 11 | 0.001155 |
| 1.230000e+02 | 11 | 0.001155 |
| 1.400000e+03 | 11 | 0.001155 |
| 1.803200e+02 | 11 | 0.001155 |
| 1.094400e+02 | 11 | 0.001155 |
| 1.420000e+02 | 11 | 0.001155 |
| 2.160000e+03 | 10 | 0.001050 |
| 8.800000e+02 | 10 | 0.001050 |
| 3.460800e+02 | 10 | 0.001050 |
| 1.010000e+02 | 10 | 0.001050 |
| 2.550000e+02 | 10 | 0.001050 |
| 4.800000e+03 | 10 | 0.001050 |
| 4.700000e+01 | 10 | 0.001050 |
| 1.202000e+03 | 9 | 0.000945 |
| 9.996000e+01 | 9 | 0.000945 |
| 2.040000e+03 | 9 | 0.000945 |
| 1.100000e+03 | 9 | 0.000945 |
| 3.160000e+02 | 9 | 0.000945 |
| 1.081600e+02 | 9 | 0.000945 |
| 6.924000e+02 | 9 | 0.000945 |
| 1.719600e+02 | 9 | 0.000945 |
| 1.920000e+03 | 9 | 0.000945 |
| 1.900000e+01 | 9 | 0.000945 |
| 2.020000e+02 | 9 | 0.000945 |
| 4.700000e+02 | 9 | 0.000945 |
| 2.560000e+02 | 9 | 0.000945 |
| 1.380000e+03 | 9 | 0.000945 |
| 1.040000e+03 | 8 | 0.000840 |
| 8.300000e+01 | 8 | 0.000840 |
| 2.220000e+02 | 8 | 0.000840 |
| 1.824000e+02 | 8 | 0.000840 |
| 2.900000e+01 | 8 | 0.000840 |
| 8.100000e+01 | 8 | 0.000840 |
| 9.020000e+00 | 8 | 0.000840 |
| 6.611000e+01 | 8 | 0.000840 |
| 3.012000e+01 | 8 | 0.000840 |
| 3.320000e+02 | 8 | 0.000840 |
| 2.960000e+02 | 8 | 0.000840 |
| 3.280000e+02 | 8 | 0.000840 |
| 1.820000e+02 | 8 | 0.000840 |
| 8.416000e+01 | 7 | 0.000735 |
| 8.160000e+02 | 7 | 0.000735 |
| 9.612000e+01 | 7 | 0.000735 |
| 2.850000e+02 | 7 | 0.000735 |
| 1.180000e+02 | 7 | 0.000735 |
| 2.163600e+03 | 7 | 0.000735 |
| 4.300000e+02 | 7 | 0.000735 |
| 4.900000e+01 | 7 | 0.000735 |
| 2.160000e+01 | 7 | 0.000735 |
| 7.200000e+03 | 7 | 0.000735 |
| 5.880000e+02 | 7 | 0.000735 |
| 1.860000e+02 | 7 | 0.000735 |
| 9.200000e+02 | 7 | 0.000735 |
| 5.040000e+01 | 7 | 0.000735 |
| 3.760000e+02 | 7 | 0.000735 |
| 2.100000e+03 | 7 | 0.000735 |
| 8.200000e+02 | 7 | 0.000735 |
| 1.082000e+02 | 7 | 0.000735 |
| 5.052000e+01 | 6 | 0.000630 |
| 3.050000e+02 | 6 | 0.000630 |
| 1.204000e+01 | 6 | 0.000630 |
| 3.004800e+02 | 6 | 0.000630 |
| 9.840000e+02 | 6 | 0.000630 |
| 1.503000e+01 | 6 | 0.000630 |
| 9.100000e+01 | 6 | 0.000630 |
| 8.520000e+02 | 6 | 0.000630 |
| 3.726400e+02 | 6 | 0.000630 |
| 3.080000e+02 | 6 | 0.000630 |
| 4.480000e+02 | 6 | 0.000630 |
| 4.507000e+01 | 6 | 0.000630 |
| 3.606000e+03 | 6 | 0.000630 |
| 9.036000e+01 | 6 | 0.000630 |
| 2.404100e+02 | 6 | 0.000630 |
| 8.500000e+02 | 6 | 0.000630 |
| 8.880000e+02 | 6 | 0.000630 |
| 3.606120e+03 | 5 | 0.000525 |
| 3.010000e+00 | 5 | 0.000525 |
| 1.322000e+02 | 5 | 0.000525 |
| 4.880000e+02 | 5 | 0.000525 |
| 2.403600e+02 | 5 | 0.000525 |
| 1.440000e+01 | 5 | 0.000525 |
| 1.620000e+03 | 5 | 0.000525 |
| 2.450000e+02 | 5 | 0.000525 |
| 6.490800e+02 | 5 | 0.000525 |
| 4.160000e+02 | 5 | 0.000525 |
| 1.210000e+02 | 5 | 0.000525 |
| 9.700000e+01 | 5 | 0.000525 |
| 1.250000e+03 | 5 | 0.000525 |
| 8.796000e+01 | 5 | 0.000525 |
| 7.400000e+02 | 5 | 0.000525 |
| 9.010000e+00 | 5 | 0.000525 |
| 3.680000e+02 | 5 | 0.000525 |
| 9.240000e+02 | 5 | 0.000525 |
| 1.120000e+03 | 5 | 0.000525 |
| 3.880000e+02 | 4 | 0.000420 |
| 2.010000e+02 | 4 | 0.000420 |
| 5.000000e+03 | 4 | 0.000420 |
| 1.610000e+02 | 4 | 0.000420 |
| 8.760000e+02 | 4 | 0.000420 |
| 3.520000e+02 | 4 | 0.000420 |
| 4.320000e+01 | 4 | 0.000420 |
| 4.000000e+03 | 4 | 0.000420 |
| 2.740000e+02 | 4 | 0.000420 |
| 4.750000e+02 | 4 | 0.000420 |
| 1.030000e+02 | 4 | 0.000420 |
| 2.650000e+02 | 4 | 0.000420 |
| 9.120000e+02 | 4 | 0.000420 |
| 2.340000e+02 | 4 | 0.000420 |
| 1.001500e+02 | 4 | 0.000420 |
| 3.900000e+03 | 4 | 0.000420 |
| 1.021700e+02 | 4 | 0.000420 |
| 4.240000e+02 | 4 | 0.000420 |
| 9.960000e+02 | 4 | 0.000420 |
| 1.660000e+02 | 4 | 0.000420 |
| 1.870000e+02 | 4 | 0.000420 |
| 1.502000e+01 | 4 | 0.000420 |
| 3.606100e+02 | 4 | 0.000420 |
| 1.202040e+03 | 4 | 0.000420 |
| 6.250000e+02 | 4 | 0.000420 |
| 1.800000e+04 | 4 | 0.000420 |
| 7.200000e+00 | 4 | 0.000420 |
| 3.150000e+02 | 4 | 0.000420 |
| 9.900000e+01 | 4 | 0.000420 |
| 4.005000e+01 | 4 | 0.000420 |
| 5.408400e+02 | 4 | 0.000420 |
| 1.490000e+02 | 4 | 0.000420 |
| 2.860000e+02 | 4 | 0.000420 |
| 3.450000e+02 | 4 | 0.000420 |
| 7.210000e+00 | 4 | 0.000420 |
| 7.080000e+02 | 4 | 0.000420 |
| 1.460000e+02 | 4 | 0.000420 |
| 7.932000e+01 | 4 | 0.000420 |
| 1.940000e+02 | 4 | 0.000420 |
| 4.150000e+02 | 4 | 0.000420 |
| 4.507500e+02 | 4 | 0.000420 |
| 3.004000e+01 | 4 | 0.000420 |
| 1.370000e+02 | 3 | 0.000315 |
| 9.999600e+02 | 3 | 0.000315 |
| 4.120000e+02 | 3 | 0.000315 |
| 7.100000e+01 | 3 | 0.000315 |
| 1.510000e+02 | 3 | 0.000315 |
| 7.228800e+02 | 3 | 0.000315 |
| 1.128000e+03 | 3 | 0.000315 |
| 1.300000e+03 | 3 | 0.000315 |
| 2.880000e+03 | 3 | 0.000315 |
| 7.212100e+02 | 3 | 0.000315 |
| 6.922800e+02 | 3 | 0.000315 |
| 4.806000e+02 | 3 | 0.000315 |
| 2.620000e+02 | 3 | 0.000315 |
| 1.130000e+02 | 3 | 0.000315 |
| 1.464000e+03 | 3 | 0.000315 |
| 7.010000e+01 | 3 | 0.000315 |
| 1.890000e+02 | 3 | 0.000315 |
| 3.200000e+03 | 3 | 0.000315 |
| 6.510000e+01 | 3 | 0.000315 |
| 8.280000e+02 | 3 | 0.000315 |
| 6.005000e+01 | 3 | 0.000315 |
| 1.580000e+02 | 3 | 0.000315 |
| 1.280000e+03 | 3 | 0.000315 |
| 4.200000e+03 | 3 | 0.000315 |
| 9.720000e+02 | 3 | 0.000315 |
| 2.409600e+02 | 3 | 0.000315 |
| 8.414000e+02 | 3 | 0.000315 |
| 1.224000e+03 | 3 | 0.000315 |
| 8.900000e+01 | 3 | 0.000315 |
| 7.250000e+02 | 3 | 0.000315 |
| 1.482400e+02 | 3 | 0.000315 |
| 3.505000e+01 | 3 | 0.000315 |
| 1.732000e+01 | 3 | 0.000315 |
| 1.284000e+03 | 3 | 0.000315 |
| 3.560000e+02 | 3 | 0.000315 |
| 3.365600e+02 | 3 | 0.000315 |
| 5.100000e+02 | 3 | 0.000315 |
| 4.687200e+02 | 3 | 0.000315 |
| 9.372000e+01 | 3 | 0.000315 |
| 1.730000e+02 | 3 | 0.000315 |
| 9.375600e+02 | 3 | 0.000315 |
| 4.206000e+02 | 3 | 0.000315 |
| 8.600000e+02 | 3 | 0.000315 |
| 3.330000e+02 | 3 | 0.000315 |
| 3.966000e+02 | 3 | 0.000315 |
| 1.599600e+02 | 3 | 0.000315 |
| 2.644400e+02 | 3 | 0.000315 |
| 7.212120e+03 | 3 | 0.000315 |
| 3.060000e+02 | 3 | 0.000315 |
| 1.000000e+00 | 3 | 0.000315 |
| 5.760000e+01 | 3 | 0.000315 |
| 9.212000e+01 | 3 | 0.000315 |
| 4.330000e+00 | 2 | 0.000210 |
| 7.200000e+04 | 2 | 0.000210 |
| 2.239200e+02 | 2 | 0.000210 |
| 1.570000e+02 | 2 | 0.000210 |
| 7.992000e+01 | 2 | 0.000210 |
| 1.530000e+02 | 2 | 0.000210 |
| 3.605000e+01 | 2 | 0.000210 |
| 5.046000e+02 | 2 | 0.000210 |
| 1.262100e+02 | 2 | 0.000210 |
| 2.380000e+02 | 2 | 0.000210 |
| 5.592000e+01 | 2 | 0.000210 |
| 1.999200e+02 | 2 | 0.000210 |
| 3.005200e+02 | 2 | 0.000210 |
| 2.400000e+04 | 2 | 0.000210 |
| 4.460000e+02 | 2 | 0.000210 |
| 3.460000e+02 | 2 | 0.000210 |
| 4.360000e+02 | 2 | 0.000210 |
| 5.409600e+02 | 2 | 0.000210 |
| 7.612000e+01 | 2 | 0.000210 |
| 9.012000e+02 | 2 | 0.000210 |
| 1.872000e+02 | 2 | 0.000210 |
| 2.500000e+03 | 2 | 0.000210 |
| 5.360000e+02 | 2 | 0.000210 |
| 3.666000e+01 | 2 | 0.000210 |
| 2.000400e+02 | 2 | 0.000210 |
| 1.070000e+02 | 2 | 0.000210 |
| 1.360000e+03 | 2 | 0.000210 |
| 1.110000e+02 | 2 | 0.000210 |
| 1.980000e+03 | 2 | 0.000210 |
| 1.090000e+02 | 2 | 0.000210 |
| 1.594000e+02 | 2 | 0.000210 |
| 5.700000e+02 | 2 | 0.000210 |
| 1.562400e+02 | 2 | 0.000210 |
| 2.180000e+02 | 2 | 0.000210 |
| 1.282000e+02 | 2 | 0.000210 |
| 4.320000e+03 | 2 | 0.000210 |
| 1.009200e+02 | 2 | 0.000210 |
| 9.015200e+02 | 2 | 0.000210 |
| 5.900000e+01 | 2 | 0.000210 |
| 2.140000e+02 | 2 | 0.000210 |
| 1.602000e+02 | 2 | 0.000210 |
| 1.670000e+02 | 2 | 0.000210 |
| 1.225200e+02 | 2 | 0.000210 |
| 1.056000e+03 | 2 | 0.000210 |
| 2.880000e+01 | 2 | 0.000210 |
| 2.763600e+02 | 2 | 0.000210 |
| 5.999000e+01 | 2 | 0.000210 |
| 8.100000e+02 | 2 | 0.000210 |
| 1.270000e+02 | 2 | 0.000210 |
| 5.202000e+01 | 2 | 0.000210 |
| 2.406000e+02 | 2 | 0.000210 |
| 2.307600e+02 | 2 | 0.000210 |
| 3.020000e+02 | 2 | 0.000210 |
| 4.360000e+03 | 2 | 0.000210 |
| 9.496000e+01 | 2 | 0.000210 |
| 1.470000e+02 | 2 | 0.000210 |
| 5.406000e+02 | 2 | 0.000210 |
| 3.660000e+02 | 2 | 0.000210 |
| 1.202020e+03 | 2 | 0.000210 |
| 1.658400e+02 | 2 | 0.000210 |
| 1.212000e+03 | 2 | 0.000210 |
| 6.900000e+01 | 2 | 0.000210 |
| 9.324000e+01 | 2 | 0.000210 |
| 3.885000e+01 | 2 | 0.000210 |
| 2.820000e+02 | 2 | 0.000210 |
| 1.081840e+03 | 2 | 0.000210 |
| 3.180000e+02 | 2 | 0.000210 |
| 2.940000e+02 | 2 | 0.000210 |
| 1.982400e+02 | 2 | 0.000210 |
| 1.596000e+03 | 2 | 0.000210 |
| 2.950000e+02 | 2 | 0.000210 |
| 1.586400e+02 | 2 | 0.000210 |
| 8.660000e+00 | 2 | 0.000210 |
| 1.983300e+02 | 2 | 0.000210 |
| 5.300000e+02 | 2 | 0.000210 |
| 5.850000e+02 | 2 | 0.000210 |
| 2.499600e+02 | 2 | 0.000210 |
| 2.704000e+01 | 2 | 0.000210 |
| 1.092000e+03 | 2 | 0.000210 |
| 4.508000e+01 | 2 | 0.000210 |
| 7.572000e+01 | 2 | 0.000210 |
| 5.250000e+02 | 2 | 0.000210 |
| 1.322200e+02 | 2 | 0.000210 |
| 3.620000e+02 | 2 | 0.000210 |
| 3.996000e+01 | 2 | 0.000210 |
| 1.200000e+00 | 2 | 0.000210 |
| 9.960000e+01 | 2 | 0.000210 |
| 5.289000e+01 | 2 | 0.000210 |
| 1.104000e+03 | 2 | 0.000210 |
| 2.700000e+03 | 2 | 0.000210 |
| 2.200000e+03 | 2 | 0.000210 |
| 1.770000e+02 | 2 | 0.000210 |
| 7.212200e+02 | 2 | 0.000210 |
| 7.900000e+01 | 2 | 0.000210 |
| 1.170000e+02 | 2 | 0.000210 |
| 1.250000e+01 | 2 | 0.000210 |
| 1.060000e+03 | 2 | 0.000210 |
| 3.350000e+02 | 2 | 0.000210 |
| 6.242400e+02 | 2 | 0.000210 |
| 8.640000e+01 | 2 | 0.000210 |
| 4.205000e+01 | 2 | 0.000210 |
| 2.379600e+02 | 2 | 0.000210 |
| 2.043200e+02 | 2 | 0.000210 |
| 1.860000e+03 | 2 | 0.000210 |
| 2.103000e+01 | 2 | 0.000210 |
| 1.321200e+02 | 1 | 0.000105 |
| 1.644000e+03 | 1 | 0.000105 |
| 3.901500e+02 | 1 | 0.000105 |
| 2.804000e+02 | 1 | 0.000105 |
| 1.830000e+02 | 1 | 0.000105 |
| 6.060000e+01 | 1 | 0.000105 |
| 1.001000e+02 | 1 | 0.000105 |
| 1.050000e+01 | 1 | 0.000105 |
| 8.166000e+03 | 1 | 0.000105 |
| 5.772000e+01 | 1 | 0.000105 |
| 2.340000e+03 | 1 | 0.000105 |
| 1.602500e+02 | 1 | 0.000105 |
| 7.812000e+02 | 1 | 0.000105 |
| 9.800000e+02 | 1 | 0.000105 |
| 7.100000e+02 | 1 | 0.000105 |
| 1.393600e+02 | 1 | 0.000105 |
| 2.199720e+03 | 1 | 0.000105 |
| 1.803030e+03 | 1 | 0.000105 |
| 1.880400e+02 | 1 | 0.000105 |
| 9.840000e+03 | 1 | 0.000105 |
| 6.360000e+01 | 1 | 0.000105 |
| 8.120000e+02 | 1 | 0.000105 |
| 8.976000e+01 | 1 | 0.000105 |
| 2.004000e+03 | 1 | 0.000105 |
| 1.734000e+01 | 1 | 0.000105 |
| 8.016000e+01 | 1 | 0.000105 |
| 1.975200e+02 | 1 | 0.000105 |
| 5.870000e+00 | 1 | 0.000105 |
| 2.919600e+02 | 1 | 0.000105 |
| 6.750000e+02 | 1 | 0.000105 |
| 2.928000e+02 | 1 | 0.000105 |
| 2.520000e+03 | 1 | 0.000105 |
| 3.648000e+02 | 1 | 0.000105 |
| 6.610000e+01 | 1 | 0.000105 |
| 1.226400e+02 | 1 | 0.000105 |
| 1.220400e+02 | 1 | 0.000105 |
| 3.220000e+02 | 1 | 0.000105 |
| 2.167200e+02 | 1 | 0.000105 |
| 2.760000e+03 | 1 | 0.000105 |
| 3.768000e+02 | 1 | 0.000105 |
| 4.802400e+02 | 1 | 0.000105 |
| 3.439200e+02 | 1 | 0.000105 |
| 4.692000e+01 | 1 | 0.000105 |
| 8.652000e+02 | 1 | 0.000105 |
| 1.200100e+02 | 1 | 0.000105 |
| 1.033720e+03 | 1 | 0.000105 |
| 1.298160e+03 | 1 | 0.000105 |
| 3.846400e+02 | 1 | 0.000105 |
| 8.040000e+01 | 1 | 0.000105 |
| 1.692000e+03 | 1 | 0.000105 |
| 2.016000e+03 | 1 | 0.000105 |
| 1.644000e+02 | 1 | 0.000105 |
| 1.801000e+02 | 1 | 0.000105 |
| 3.360000e+01 | 1 | 0.000105 |
| 5.460000e+01 | 1 | 0.000105 |
| 3.000000e+05 | 1 | 0.000105 |
| 3.040000e+01 | 1 | 0.000105 |
| 2.253800e+03 | 1 | 0.000105 |
| 2.439600e+02 | 1 | 0.000105 |
| 6.720000e+01 | 1 | 0.000105 |
| 2.060000e+02 | 1 | 0.000105 |
| 1.242000e+02 | 1 | 0.000105 |
| 1.402000e+02 | 1 | 0.000105 |
| 1.390000e+02 | 1 | 0.000105 |
| 6.235200e+02 | 1 | 0.000105 |
| 3.996000e+02 | 1 | 0.000105 |
| 1.310000e+02 | 1 | 0.000105 |
| 3.030000e+02 | 1 | 0.000105 |
| 7.960000e+02 | 1 | 0.000105 |
| 6.613200e+02 | 1 | 0.000105 |
| 1.665600e+02 | 1 | 0.000105 |
| 7.920000e+01 | 1 | 0.000105 |
| 2.796000e+02 | 1 | 0.000105 |
| 1.356000e+03 | 1 | 0.000105 |
| 6.696000e+02 | 1 | 0.000105 |
| 5.720000e+02 | 1 | 0.000105 |
| 8.245600e+02 | 1 | 0.000105 |
| 3.800000e+03 | 1 | 0.000105 |
| 2.476800e+02 | 1 | 0.000105 |
| 2.560000e+01 | 1 | 0.000105 |
| 1.201000e+02 | 1 | 0.000105 |
| 2.388000e+02 | 1 | 0.000105 |
| 9.014000e+01 | 1 | 0.000105 |
| 9.732000e+02 | 1 | 0.000105 |
| 3.380000e+02 | 1 | 0.000105 |
| 5.920000e+02 | 1 | 0.000105 |
| 2.811600e+02 | 1 | 0.000105 |
| 3.125200e+02 | 1 | 0.000105 |
| 2.601000e+02 | 1 | 0.000105 |
| 2.580000e+02 | 1 | 0.000105 |
| 1.117800e+03 | 1 | 0.000105 |
| 1.622400e+02 | 1 | 0.000105 |
| 1.802000e+01 | 1 | 0.000105 |
| 9.600000e+00 | 1 | 0.000105 |
| 4.660800e+02 | 1 | 0.000105 |
| 4.086000e+01 | 1 | 0.000105 |
| 1.381500e+02 | 1 | 0.000105 |
| 1.322000e+01 | 1 | 0.000105 |
| 3.840000e+01 | 1 | 0.000105 |
| 4.620000e+02 | 1 | 0.000105 |
| 2.164000e+01 | 1 | 0.000105 |
| 2.404400e+02 | 1 | 0.000105 |
| 2.721200e+02 | 1 | 0.000105 |
| 2.640000e+03 | 1 | 0.000105 |
| 3.028800e+02 | 1 | 0.000105 |
| 6.015000e+01 | 1 | 0.000105 |
| 5.580000e+02 | 1 | 0.000105 |
| 1.804000e+01 | 1 | 0.000105 |
| 2.018400e+02 | 1 | 0.000105 |
| 1.221200e+02 | 1 | 0.000105 |
| 3.110000e+02 | 1 | 0.000105 |
| 5.196000e+02 | 1 | 0.000105 |
| 3.020000e+03 | 1 | 0.000105 |
| 9.999000e+01 | 1 | 0.000105 |
| 1.141800e+02 | 1 | 0.000105 |
| 3.740000e+02 | 1 | 0.000105 |
| 1.410000e+02 | 1 | 0.000105 |
| 3.230000e+02 | 1 | 0.000105 |
| 1.240800e+02 | 1 | 0.000105 |
| 4.080000e+03 | 1 | 0.000105 |
| 9.680000e+02 | 1 | 0.000105 |
| 5.908000e+01 | 1 | 0.000105 |
| 5.160000e+01 | 1 | 0.000105 |
| 3.003600e+02 | 1 | 0.000105 |
| 3.363600e+02 | 1 | 0.000105 |
| 2.080800e+02 | 1 | 0.000105 |
| 1.430000e+02 | 1 | 0.000105 |
| 3.750000e+01 | 1 | 0.000105 |
| 2.884900e+02 | 1 | 0.000105 |
| 7.788000e+02 | 1 | 0.000105 |
| 2.260000e+02 | 1 | 0.000105 |
| 1.892400e+02 | 1 | 0.000105 |
| 5.408000e+01 | 1 | 0.000105 |
| 1.402000e+01 | 1 | 0.000105 |
| 2.800000e+03 | 1 | 0.000105 |
| 2.242400e+02 | 1 | 0.000105 |
| 1.382300e+02 | 1 | 0.000105 |
| 4.928400e+02 | 1 | 0.000105 |
| 2.524000e+02 | 1 | 0.000105 |
| 4.808100e+02 | 1 | 0.000105 |
| 1.009680e+03 | 1 | 0.000105 |
| 5.560000e+02 | 1 | 0.000105 |
| 9.810000e+01 | 1 | 0.000105 |
| 1.501500e+02 | 1 | 0.000105 |
| 1.658800e+02 | 1 | 0.000105 |
| 3.850000e+02 | 1 | 0.000105 |
| 4.500000e+03 | 1 | 0.000105 |
| 6.040000e+02 | 1 | 0.000105 |
| 5.507500e+02 | 1 | 0.000105 |
| 1.522400e+02 | 1 | 0.000105 |
| 9.096000e+01 | 1 | 0.000105 |
| 1.002000e+02 | 1 | 0.000105 |
| 9.015100e+02 | 1 | 0.000105 |
| 4.189200e+02 | 1 | 0.000105 |
| 1.215000e+03 | 1 | 0.000105 |
| 1.027200e+02 | 1 | 0.000105 |
| 7.320000e+01 | 1 | 0.000105 |
| 4.840000e+02 | 1 | 0.000105 |
| 1.512000e+03 | 1 | 0.000105 |
| 1.268400e+02 | 1 | 0.000105 |
| 1.297200e+02 | 1 | 0.000105 |
| 1.110000e+03 | 1 | 0.000105 |
| 2.804000e+01 | 1 | 0.000105 |
| 2.253500e+02 | 1 | 0.000105 |
| 3.002000e+02 | 1 | 0.000105 |
| 3.334800e+02 | 1 | 0.000105 |
| 7.980000e+02 | 1 | 0.000105 |
| 2.810000e+02 | 1 | 0.000105 |
| 9.016000e+01 | 1 | 0.000105 |
| 2.884800e+03 | 1 | 0.000105 |
| 1.002000e+03 | 1 | 0.000105 |
| 2.115200e+02 | 1 | 0.000105 |
| 7.680000e+01 | 1 | 0.000105 |
| 9.075000e+01 | 1 | 0.000105 |
| 2.642400e+02 | 1 | 0.000105 |
| 3.005060e+04 | 1 | 0.000105 |
| 1.520000e+03 | 1 | 0.000105 |
| 7.513000e+01 | 1 | 0.000105 |
| 3.244800e+02 | 1 | 0.000105 |
| 4.094400e+02 | 1 | 0.000105 |
| 1.051000e+02 | 1 | 0.000105 |
| 8.240000e+02 | 1 | 0.000105 |
| 3.050000e+01 | 1 | 0.000105 |
| 1.634000e+02 | 1 | 0.000105 |
| 2.604000e+02 | 1 | 0.000105 |
| 7.001000e+01 | 1 | 0.000105 |
| 2.402000e+02 | 1 | 0.000105 |
| 1.471200e+02 | 1 | 0.000105 |
| 1.200400e+02 | 1 | 0.000105 |
| 6.001000e+01 | 1 | 0.000105 |
| 1.350000e+03 | 1 | 0.000105 |
| 4.207100e+02 | 1 | 0.000105 |
| 1.252000e+02 | 1 | 0.000105 |
| 5.050000e+02 | 1 | 0.000105 |
| 7.813200e+02 | 1 | 0.000105 |
| 3.604800e+02 | 1 | 0.000105 |
| 8.212000e+01 | 1 | 0.000105 |
| 1.485000e+03 | 1 | 0.000105 |
| 7.620000e+01 | 1 | 0.000105 |
| 5.528000e+01 | 1 | 0.000105 |
| 1.680000e+01 | 1 | 0.000105 |
| 1.806000e+01 | 1 | 0.000105 |
| 9.080000e+00 | 1 | 0.000105 |
| 4.327000e+01 | 1 | 0.000105 |
| 2.668800e+02 | 1 | 0.000105 |
| 1.442000e+02 | 1 | 0.000105 |
| 1.250100e+02 | 1 | 0.000105 |
| 2.632000e+01 | 1 | 0.000105 |
| 9.000000e+03 | 1 | 0.000105 |
| 7.933200e+02 | 1 | 0.000105 |
| 4.020000e+02 | 1 | 0.000105 |
| 1.502530e+03 | 1 | 0.000105 |
| 1.080000e+06 | 1 | 0.000105 |
| 1.239600e+02 | 1 | 0.000105 |
| 3.907000e+01 | 1 | 0.000105 |
| 4.399200e+02 | 1 | 0.000105 |
| 7.280000e+02 | 1 | 0.000105 |
| 5.320000e+02 | 1 | 0.000105 |
| 2.472000e+02 | 1 | 0.000105 |
| 1.253300e+02 | 1 | 0.000105 |
| 1.752000e+03 | 1 | 0.000105 |
| 2.058000e+04 | 1 | 0.000105 |
| 3.400800e+02 | 1 | 0.000105 |
| 1.164000e+06 | 1 | 0.000105 |
| 2.700500e+02 | 1 | 0.000105 |
| 2.730000e+02 | 1 | 0.000105 |
| 5.400000e+03 | 1 | 0.000105 |
| 5.949600e+02 | 1 | 0.000105 |
| 1.104000e+02 | 1 | 0.000105 |
| 3.820800e+02 | 1 | 0.000105 |
| 1.600800e+02 | 1 | 0.000105 |
| 1.141900e+02 | 1 | 0.000105 |
| 1.690000e+02 | 1 | 0.000105 |
| 1.382000e+01 | 1 | 0.000105 |
| 2.500000e+04 | 1 | 0.000105 |
| 4.380000e+02 | 1 | 0.000105 |
| 1.642400e+02 | 1 | 0.000105 |
| 2.328000e+03 | 1 | 0.000105 |
| 6.972000e+01 | 1 | 0.000105 |
| 4.020000e+01 | 1 | 0.000105 |
| 1.441200e+02 | 1 | 0.000105 |
| 7.220000e+00 | 1 | 0.000105 |
| 7.710000e+01 | 1 | 0.000105 |
| 1.280100e+02 | 1 | 0.000105 |
| 2.956920e+03 | 1 | 0.000105 |
| 7.230000e+01 | 1 | 0.000105 |
| 1.990000e+02 | 1 | 0.000105 |
| 1.950000e+01 | 1 | 0.000105 |
| 1.750000e+03 | 1 | 0.000105 |
| 5.750000e+02 | 1 | 0.000105 |
| 4.580000e+02 | 1 | 0.000105 |
| 1.200000e+05 | 1 | 0.000105 |
| 4.688400e+02 | 1 | 0.000105 |
| 1.536000e+03 | 1 | 0.000105 |
| 2.704500e+02 | 1 | 0.000105 |
| 1.261500e+02 | 1 | 0.000105 |
| 1.117920e+03 | 1 | 0.000105 |
| 1.502400e+02 | 1 | 0.000105 |
| 2.103200e+02 | 1 | 0.000105 |
| 1.081200e+03 | 1 | 0.000105 |
| 3.180000e+03 | 1 | 0.000105 |
| 4.352400e+02 | 1 | 0.000105 |
| 2.003000e+01 | 1 | 0.000105 |
| 3.065100e+02 | 1 | 0.000105 |
| 4.681200e+02 | 1 | 0.000105 |
| 7.410000e+02 | 1 | 0.000105 |
| 1.762400e+02 | 1 | 0.000105 |
| 1.710000e+02 | 1 | 0.000105 |
| 5.944800e+02 | 1 | 0.000105 |
| 1.992000e+03 | 1 | 0.000105 |
| 9.060000e+02 | 1 | 0.000105 |
| 4.510000e+02 | 1 | 0.000105 |
| 6.490000e+01 | 1 | 0.000105 |
| 3.780000e+02 | 1 | 0.000105 |
| 7.208000e+02 | 1 | 0.000105 |
| 3.588000e+01 | 1 | 0.000105 |
| 1.502400e+03 | 1 | 0.000105 |
| 1.330000e+02 | 1 | 0.000105 |
| 2.043600e+02 | 1 | 0.000105 |
| 1.333200e+02 | 1 | 0.000105 |
| 8.656000e+01 | 1 | 0.000105 |
| 5.120000e+02 | 1 | 0.000105 |
| 1.513200e+02 | 1 | 0.000105 |
| 4.050000e+02 | 1 | 0.000105 |
| 2.127600e+02 | 1 | 0.000105 |
| 1.443000e+02 | 1 | 0.000105 |
| 7.512700e+02 | 1 | 0.000105 |
| 6.130800e+02 | 1 | 0.000105 |
| 3.305000e+01 | 1 | 0.000105 |
| 9.010000e+01 | 1 | 0.000105 |
| 1.800000e+00 | 1 | 0.000105 |
| 1.226040e+03 | 1 | 0.000105 |
| 2.780000e+02 | 1 | 0.000105 |
| 6.080000e+02 | 1 | 0.000105 |
| 9.616000e+02 | 1 | 0.000105 |
| 5.902000e+01 | 1 | 0.000105 |
| 2.619600e+02 | 1 | 0.000105 |
| 3.205000e+02 | 1 | 0.000105 |
| 6.410000e+02 | 1 | 0.000105 |
| 6.910000e+01 | 1 | 0.000105 |
| 2.700300e+02 | 1 | 0.000105 |
| 2.401200e+02 | 1 | 0.000105 |
| 9.010000e+02 | 1 | 0.000105 |
| 1.622700e+02 | 1 | 0.000105 |
| 1.203000e+03 | 1 | 0.000105 |
| 2.901000e+02 | 1 | 0.000105 |
| 1.682400e+02 | 1 | 0.000105 |
| 2.090000e+02 | 1 | 0.000105 |
| 3.205000e+01 | 1 | 0.000105 |
| 6.300000e+02 | 1 | 0.000105 |
| 1.003200e+02 | 1 | 0.000105 |
| 2.280000e+03 | 1 | 0.000105 |
| 8.000000e+03 | 1 | 0.000105 |
| 5.440000e+02 | 1 | 0.000105 |
| 1.008000e+02 | 1 | 0.000105 |
| 1.584000e+03 | 1 | 0.000105 |
| 2.184000e+03 | 1 | 0.000105 |
| 1.116000e+03 | 1 | 0.000105 |
| 4.640000e+02 | 1 | 0.000105 |
| 2.524200e+02 | 1 | 0.000105 |
| 5.650000e+02 | 1 | 0.000105 |
| 3.480000e+03 | 1 | 0.000105 |
| 4.208000e+01 | 1 | 0.000105 |
| 7.995600e+02 | 1 | 0.000105 |
| 6.586000e+01 | 1 | 0.000105 |
| 3.612000e+03 | 1 | 0.000105 |
| 7.927200e+02 | 1 | 0.000105 |
| 2.770000e+02 | 1 | 0.000105 |
| 1.436400e+02 | 1 | 0.000105 |
| 7.809566e+09 | 1 | 0.000105 |
| 3.000000e+04 | 1 | 0.000105 |
| 1.159200e+02 | 1 | 0.000105 |
| 1.992000e+02 | 1 | 0.000105 |
| 3.300000e+03 | 1 | 0.000105 |
| 7.200000e-01 | 1 | 0.000105 |
| 3.889200e+02 | 1 | 0.000105 |
| 1.344000e+02 | 1 | 0.000105 |
| 1.834800e+02 | 1 | 0.000105 |
| 1.019600e+02 | 1 | 0.000105 |
| 1.359600e+02 | 1 | 0.000105 |
| 1.008000e+04 | 1 | 0.000105 |
| 7.640000e+02 | 1 | 0.000105 |
| 4.201000e+03 | 1 | 0.000105 |
| 2.070000e+02 | 1 | 0.000105 |
| 2.592000e+03 | 1 | 0.000105 |
| 1.044000e+03 | 1 | 0.000105 |
| 2.425000e+02 | 1 | 0.000105 |
| 4.484000e+01 | 1 | 0.000105 |
| 6.588000e+01 | 1 | 0.000105 |
| 1.983600e+02 | 1 | 0.000105 |
| 5.900000e+02 | 1 | 0.000105 |
| 1.160000e+03 | 1 | 0.000105 |
| 5.010000e+02 | 1 | 0.000105 |
| 1.399200e+02 | 1 | 0.000105 |
| 4.280000e+02 | 1 | 0.000105 |
| 7.196000e+01 | 1 | 0.000105 |
| 5.988000e+01 | 1 | 0.000105 |
| 2.410000e+02 | 1 | 0.000105 |
| 1.908000e+03 | 1 | 0.000105 |
| 2.064000e+03 | 1 | 0.000105 |
| 3.360000e+03 | 1 | 0.000105 |
| 3.602400e+02 | 1 | 0.000105 |
| 2.520000e+01 | 1 | 0.000105 |
| 2.803600e+02 | 1 | 0.000105 |
| 1.442430e+03 | 1 | 0.000105 |
| 6.006000e+01 | 1 | 0.000105 |
| 2.193600e+02 | 1 | 0.000105 |
| 3.006000e+01 | 1 | 0.000105 |
| 2.705000e+01 | 1 | 0.000105 |
| 2.308000e+02 | 1 | 0.000105 |
| 1.730000e+01 | 1 | 0.000105 |
| 3.860000e+02 | 1 | 0.000105 |
| 8.428800e+02 | 1 | 0.000105 |
| 6.132000e+01 | 1 | 0.000105 |
| 2.132000e+01 | 1 | 0.000105 |
| 2.451600e+02 | 1 | 0.000105 |
| 5.493000e+01 | 1 | 0.000105 |
| 6.600000e+03 | 1 | 0.000105 |
| 2.352000e+03 | 1 | 0.000105 |
| 4.666400e+02 | 1 | 0.000105 |
| 2.210000e+02 | 1 | 0.000105 |
| 1.752500e+02 | 1 | 0.000105 |
| 4.332000e+01 | 1 | 0.000105 |
| 9.014400e+02 | 1 | 0.000105 |
| 4.006000e+01 | 1 | 0.000105 |
| 4.059600e+02 | 1 | 0.000105 |
| 2.196000e+02 | 1 | 0.000105 |
| 1.009600e+02 | 1 | 0.000105 |
| 2.524200e+03 | 1 | 0.000105 |
| 4.447200e+02 | 1 | 0.000105 |
| 2.401000e+02 | 1 | 0.000105 |
| 3.000100e+02 | 1 | 0.000105 |
| 6.480000e+01 | 1 | 0.000105 |
| 1.153200e+02 | 1 | 0.000105 |
| 5.556000e+01 | 1 | 0.000105 |
| 2.115600e+02 | 1 | 0.000105 |
| 7.500000e+00 | 1 | 0.000105 |
| 1.121200e+02 | 1 | 0.000105 |
| 2.530000e+02 | 1 | 0.000105 |
| 2.430000e+02 | 1 | 0.000105 |
| 1.200800e+02 | 1 | 0.000105 |
| 2.598000e+01 | 1 | 0.000105 |
| 3.820000e+02 | 1 | 0.000105 |
| 2.890000e+02 | 1 | 0.000105 |
| 1.500000e+04 | 1 | 0.000105 |
| 1.810000e+02 | 1 | 0.000105 |
| 1.620500e+02 | 1 | 0.000105 |
| 1.514400e+02 | 1 | 0.000105 |
| 1.586640e+03 | 1 | 0.000105 |
| 1.930000e+02 | 1 | 0.000105 |
| 4.620000e+03 | 1 | 0.000105 |
| 1.204000e+03 | 1 | 0.000105 |
| 4.760000e+02 | 1 | 0.000105 |
| 1.939200e+02 | 1 | 0.000105 |
| 2.420000e+02 | 1 | 0.000105 |
| 8.400000e+03 | 1 | 0.000105 |
# Vamos a realizar analisis por cada variable
var = "msf_valuetotalcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuetotalcont__c es 450726. Lo que supone un 24.99286078276873% El nº de vacios para la variable msf_valuetotalcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.0 | 119734 | 8.851528 |
| 60.0 | 89798 | 6.638461 |
| 180.0 | 61765 | 4.566077 |
| 0.0 | 56609 | 4.184911 |
| 30.0 | 55243 | 4.083927 |
| ... | ... | ... |
| 4900.0 | 1 | 0.000074 |
| 1735.0 | 1 | 0.000074 |
| 3739.0 | 1 | 0.000074 |
| 6820.0 | 1 | 0.000074 |
| 8400.0 | 1 | 0.000074 |
3222 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_valuedonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuedonorcont__c es 1178350. Lo que supone un 65.33977960751217% El nº de vacios para la variable msf_valuedonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.00 | 58321 | 9.330330 |
| 30.00 | 46197 | 7.390704 |
| 100.00 | 44776 | 7.163369 |
| 50.00 | 44696 | 7.150571 |
| 20.00 | 40864 | 6.537518 |
| ... | ... | ... |
| 99.70 | 1 | 0.000160 |
| 20.80 | 1 | 0.000160 |
| 18.42 | 1 | 0.000160 |
| 2255.78 | 1 | 0.000160 |
| 103.17 | 1 | 0.000160 |
11052 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastyeardonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastyeardonorvalue__c es 1690288. Lo que supone un 93.72685992550815% El nº de vacios para la variable msf_lastyeardonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.00 | 24608 | 21.751774 |
| 100.00 | 8522 | 7.532860 |
| 50.00 | 8056 | 7.120948 |
| 20.00 | 7096 | 6.272375 |
| 30.00 | 6964 | 6.155696 |
| ... | ... | ... |
| 137.50 | 1 | 0.000884 |
| 1.06 | 1 | 0.000884 |
| 331.10 | 1 | 0.000884 |
| 1102.89 | 1 | 0.000884 |
| 296.00 | 1 | 0.000884 |
1229 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_maximumdonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_maximumdonorvalue__c es 1177577. Lo que supone un 65.29691657900909% El nº de vacios para la variable msf_maximumdonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.00 | 64616 | 10.324651 |
| 100.00 | 53088 | 8.482652 |
| 60.00 | 47996 | 7.669028 |
| 30.00 | 47179 | 7.538484 |
| 50.00 | 44401 | 7.094602 |
| ... | ... | ... |
| 1082.79 | 1 | 0.000160 |
| 20.80 | 1 | 0.000160 |
| 2255.78 | 1 | 0.000160 |
| 101.37 | 1 | 0.000160 |
| 70.96 | 1 | 0.000160 |
10298 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_averagedonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_averagedonorvalue__c es 1177577. Lo que supone un 65.29691657900909% El nº de vacios para la variable msf_averagedonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.00 | 64612 | 10.324011 |
| 30.00 | 36150 | 5.776218 |
| 10.00 | 32521 | 5.196359 |
| 20.00 | 30276 | 4.837643 |
| 50.00 | 29166 | 4.660282 |
| ... | ... | ... |
| 536.82 | 1 | 0.000160 |
| 124.11 | 1 | 0.000160 |
| 253.67 | 1 | 0.000160 |
| 187.58 | 1 | 0.000160 |
| 897.22 | 1 | 0.000160 |
28206 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lifetime__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lifetime__c es 507098. Lo que supone un 28.11870120033115% El nº de vacios para la variable msf_lifetime__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c', 'msf_lifetime__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 431308 | 33.271697 |
| 1.0 | 91282 | 7.041620 |
| 2.0 | 68523 | 5.285959 |
| 3.0 | 64013 | 4.938052 |
| 6.0 | 61296 | 4.728458 |
| 7.0 | 59518 | 4.591301 |
| 4.0 | 58340 | 4.500429 |
| 5.0 | 58229 | 4.491866 |
| 8.0 | 56124 | 4.329483 |
| 9.0 | 39173 | 3.021860 |
| 10.0 | 32892 | 2.537335 |
| 11.0 | 31509 | 2.430648 |
| 12.0 | 29106 | 2.245277 |
| 13.0 | 26715 | 2.060832 |
| 14.0 | 24031 | 1.853785 |
| 17.0 | 19022 | 1.467383 |
| 18.0 | 18574 | 1.432824 |
| 16.0 | 17818 | 1.374505 |
| 15.0 | 16885 | 1.302532 |
| 19.0 | 14678 | 1.132281 |
| 20.0 | 11721 | 0.904174 |
| 28.0 | 10901 | 0.840918 |
| 23.0 | 9104 | 0.702295 |
| 22.0 | 7547 | 0.582186 |
| 24.0 | 6730 | 0.519162 |
| 21.0 | 6236 | 0.481054 |
| 29.0 | 5669 | 0.437315 |
| 25.0 | 5256 | 0.405455 |
| 26.0 | 4557 | 0.351533 |
| 27.0 | 4406 | 0.339885 |
| 30.0 | 3922 | 0.302549 |
| 31.0 | 739 | 0.057007 |
| 32.0 | 190 | 0.014657 |
| 34.0 | 141 | 0.010877 |
| 33.0 | 102 | 0.007868 |
| 35.0 | 50 | 0.003857 |
| 36.0 | 14 | 0.001080 |
# Vamos a realizar analisis por cada variable
var = "msf_commitment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_commitment__c es 223407. Lo que supone un 12.387969739699981% El nº de vacios para la variable msf_commitment__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 1172390 | 74.201335 |
| 1.0 | 230295 | 14.575522 |
| 2.0 | 99316 | 6.285775 |
| 3.0 | 36324 | 2.298970 |
| 4.0 | 17949 | 1.136004 |
| 5.0 | 9412 | 0.595692 |
| 6.0 | 5366 | 0.339618 |
| 7.0 | 3184 | 0.201517 |
| 8.0 | 1909 | 0.120822 |
| 9.0 | 1194 | 0.075569 |
| 10.0 | 733 | 0.046392 |
| 11.0 | 527 | 0.033354 |
| 12.0 | 342 | 0.021645 |
| 13.0 | 244 | 0.015443 |
| 14.0 | 169 | 0.010696 |
| 15.0 | 123 | 0.007785 |
| 16.0 | 113 | 0.007152 |
| 17.0 | 76 | 0.004810 |
| 18.0 | 54 | 0.003418 |
| 19.0 | 41 | 0.002595 |
| 20.0 | 35 | 0.002215 |
| 21.0 | 34 | 0.002152 |
| 22.0 | 22 | 0.001392 |
| 23.0 | 20 | 0.001266 |
| 24.0 | 15 | 0.000949 |
| 29.0 | 15 | 0.000949 |
| 25.0 | 14 | 0.000886 |
| 26.0 | 11 | 0.000696 |
| 27.0 | 9 | 0.000570 |
| 30.0 | 9 | 0.000570 |
| 28.0 | 8 | 0.000506 |
| 32.0 | 7 | 0.000443 |
| 31.0 | 7 | 0.000443 |
| 33.0 | 5 | 0.000316 |
| 36.0 | 5 | 0.000316 |
| 43.0 | 4 | 0.000253 |
| 38.0 | 4 | 0.000253 |
| 34.0 | 3 | 0.000190 |
| 35.0 | 3 | 0.000190 |
| 61.0 | 2 | 0.000127 |
| 46.0 | 2 | 0.000127 |
| 37.0 | 2 | 0.000127 |
| 42.0 | 2 | 0.000127 |
| 80.0 | 1 | 0.000063 |
| 56.0 | 1 | 0.000063 |
| 39.0 | 1 | 0.000063 |
| 47.0 | 1 | 0.000063 |
| 57.0 | 1 | 0.000063 |
| 72.0 | 1 | 0.000063 |
| 52.0 | 1 | 0.000063 |
| 71.0 | 1 | 0.000063 |
| 83.0 | 1 | 0.000063 |
| 93.0 | 1 | 0.000063 |
| 53.0 | 1 | 0.000063 |
| 45.0 | 1 | 0.000063 |
| 54.0 | 1 | 0.000063 |
# Vamos a analizar la tabla contactos
df=df_contactos[df_contactos["msf_isactiverecurringdonor__c"]=="Socio"]
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_contactos_f=list()
# Vamos a realizar analisis por cada variable
var = "msf_seniority__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_seniority__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_seniority__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7.0 | 37151 | 7.704096 |
| 8.0 | 35920 | 7.448820 |
| 6.0 | 35577 | 7.377692 |
| 9.0 | 32825 | 6.807003 |
| 5.0 | 24934 | 5.170626 |
| 10.0 | 21839 | 4.528808 |
| 4.0 | 21695 | 4.498947 |
| 12.0 | 20717 | 4.296136 |
| 1.0 | 20219 | 4.192865 |
| 13.0 | 18683 | 3.874341 |
| 2.0 | 18281 | 3.790977 |
| 11.0 | 18190 | 3.772106 |
| 3.0 | 16884 | 3.501277 |
| 14.0 | 16837 | 3.491531 |
| 18.0 | 15434 | 3.200587 |
| 0.0 | 14007 | 2.904667 |
| 17.0 | 13416 | 2.782110 |
| 19.0 | 12250 | 2.540313 |
| 16.0 | 12150 | 2.519576 |
| 15.0 | 11875 | 2.462549 |
| 29.0 | 10158 | 2.106490 |
| 20.0 | 8992 | 1.864694 |
| 23.0 | 7020 | 1.455755 |
| 22.0 | 5634 | 1.168337 |
| 28.0 | 4958 | 1.028153 |
| 24.0 | 4955 | 1.027531 |
| 21.0 | 4912 | 1.018614 |
| 25.0 | 4746 | 0.984190 |
| 27.0 | 3605 | 0.747578 |
| 31.0 | 3064 | 0.635389 |
| 26.0 | 2463 | 0.510758 |
| 30.0 | 2101 | 0.435690 |
| 32.0 | 403 | 0.083571 |
| 35.0 | 124 | 0.025714 |
| 34.0 | 92 | 0.019078 |
| 33.0 | 77 | 0.015968 |
| 36.0 | 31 | 0.006429 |
| 37.0 | 5 | 0.001037 |
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year__c"
# Analizamos nulos
count_nulos(df_contactos,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__best_gift_year__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__best_gift_year__c es 709207. Lo que supone un 39.32569192184401%
['npo02__best_gift_year__c']
# Analizamos posibles valores de la variable
freq_variables(df_contactos,var)
| # Tot | % Tot | |
|---|---|---|
| 709207 | 39.325692 | |
| 2018 | 303667 | 16.838405 |
| 2022 | 185032 | 10.260067 |
| 2021 | 93074 | 5.160975 |
| 2020 | 90828 | 5.036434 |
| 2019 | 77054 | 4.272662 |
| 2023 | 55899 | 3.099612 |
| 2010 | 29210 | 1.619701 |
| 1994 | 28224 | 1.565027 |
| 2017 | 21245 | 1.178040 |
| 2005 | 15932 | 0.883433 |
| 2014 | 14681 | 0.814065 |
| 2011 | 14643 | 0.811958 |
| 2004 | 13160 | 0.729725 |
| 2000 | 12659 | 0.701944 |
| 2015 | 11996 | 0.665181 |
| 2001 | 11403 | 0.632299 |
| 1998 | 11363 | 0.630081 |
| 2013 | 10940 | 0.606626 |
| 2016 | 9948 | 0.551619 |
| 2003 | 9537 | 0.528829 |
| 2008 | 8465 | 0.469386 |
| 1999 | 8142 | 0.451476 |
| 2009 | 7599 | 0.421366 |
| 1996 | 6869 | 0.380888 |
| 2012 | 6795 | 0.376784 |
| 2006 | 6723 | 0.372792 |
| 1992 | 6238 | 0.345899 |
| 2007 | 5562 | 0.308414 |
| 2002 | 4753 | 0.263555 |
| 1997 | 4491 | 0.249027 |
| 1995 | 4064 | 0.225350 |
| 1993 | 2470 | 0.136962 |
| 1991 | 624 | 0.034601 |
| 1989 | 435 | 0.024121 |
| 1990 | 212 | 0.011755 |
| 1988 | 187 | 0.010369 |
| 1987 | 88 | 0.004880 |
# Vamos a realizar analisis por cada variable
var = "msf_birthyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_birthyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_birthyear__c es 110441. Lo que supone un 22.902427087826403%
['npo02__best_gift_year__c', 'msf_birthyear__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 110441 | 22.902427 | |
| 1958 | 10010 | 2.075799 |
| 1959 | 9924 | 2.057965 |
| 1957 | 9901 | 2.053195 |
| 1964 | 9756 | 2.023126 |
| 1963 | 9736 | 2.018979 |
| 1960 | 9652 | 2.001559 |
| 1962 | 9554 | 1.981237 |
| 1961 | 9475 | 1.964855 |
| 1965 | 9404 | 1.950131 |
| 1956 | 9080 | 1.882942 |
| 1966 | 9012 | 1.868841 |
| 1968 | 8945 | 1.854947 |
| 1967 | 8630 | 1.789625 |
| 1955 | 8335 | 1.728450 |
| 1973 | 8203 | 1.701077 |
| 1969 | 8201 | 1.700662 |
| 1974 | 8176 | 1.695478 |
| 1971 | 8175 | 1.695270 |
| 1972 | 8166 | 1.693404 |
| 1970 | 8126 | 1.685109 |
| 1975 | 7997 | 1.658358 |
| 1954 | 7816 | 1.620824 |
| 1976 | 7566 | 1.568980 |
| 1953 | 7407 | 1.536008 |
| 1977 | 7156 | 1.483958 |
| 1952 | 7085 | 1.469234 |
| 1978 | 7080 | 1.468197 |
| 1951 | 6680 | 1.385248 |
| 1979 | 6434 | 1.334235 |
| 1950 | 6378 | 1.322622 |
| 1949 | 6107 | 1.266424 |
| 1980 | 5904 | 1.224327 |
| 1948 | 5815 | 1.205871 |
| 1981 | 5542 | 1.149258 |
| 1947 | 5161 | 1.070250 |
| 1982 | 4917 | 1.019651 |
| 1945 | 4621 | 0.958268 |
| 1946 | 4605 | 0.954950 |
| 1983 | 4540 | 0.941471 |
| 1984 | 4044 | 0.838614 |
| 1944 | 3874 | 0.803361 |
| 1943 | 3842 | 0.796725 |
| 1985 | 3614 | 0.749444 |
| 1986 | 3074 | 0.637463 |
| 1942 | 2916 | 0.604698 |
| 1987 | 2851 | 0.591219 |
| 1940 | 2601 | 0.539376 |
| 1941 | 2556 | 0.530044 |
| 1988 | 2509 | 0.520298 |
| 1989 | 2316 | 0.480275 |
| 1990 | 2203 | 0.456842 |
| 1992 | 2085 | 0.432372 |
| 1991 | 2074 | 0.430091 |
| 1993 | 1986 | 0.411842 |
| 1994 | 1907 | 0.395459 |
| 1995 | 1851 | 0.383847 |
| 1997 | 1836 | 0.380736 |
| 1996 | 1824 | 0.378247 |
| 1999 | 1707 | 0.353985 |
| 2000 | 1679 | 0.348178 |
| 1998 | 1660 | 0.344238 |
| 1939 | 1630 | 0.338017 |
| 1938 | 1539 | 0.319146 |
| 2001 | 1532 | 0.317695 |
| 1936 | 1515 | 0.314169 |
| 1937 | 1413 | 0.293017 |
| 2002 | 1273 | 0.263985 |
| 1935 | 1209 | 0.250713 |
| 2003 | 1137 | 0.235783 |
| 1934 | 1016 | 0.210690 |
| 1933 | 815 | 0.169009 |
| 2004 | 738 | 0.153041 |
| 1932 | 710 | 0.147234 |
| 1930 | 558 | 0.115714 |
| 1931 | 522 | 0.108248 |
| 1929 | 265 | 0.054954 |
| 1928 | 225 | 0.046659 |
| 1927 | 168 | 0.034839 |
| 1926 | 104 | 0.021567 |
| 2005 | 103 | 0.021359 |
| 1925 | 79 | 0.016382 |
| 2006 | 73 | 0.015138 |
| 2017 | 72 | 0.014931 |
| 2019 | 60 | 0.012442 |
| 2016 | 54 | 0.011198 |
| 2008 | 52 | 0.010783 |
| 2020 | 51 | 0.010576 |
| 2021 | 49 | 0.010161 |
| 2014 | 45 | 0.009332 |
| 2007 | 44 | 0.009124 |
| 2015 | 40 | 0.008295 |
| 1924 | 40 | 0.008295 |
| 2013 | 39 | 0.008088 |
| 2018 | 34 | 0.007051 |
| 1923 | 33 | 0.006843 |
| 2012 | 31 | 0.006429 |
| 2009 | 31 | 0.006429 |
| 2010 | 31 | 0.006429 |
| 2011 | 27 | 0.005599 |
| 1922 | 23 | 0.004770 |
| 1921 | 21 | 0.004355 |
| 1919 | 19 | 0.003940 |
| 2022 | 14 | 0.002903 |
| 1920 | 13 | 0.002696 |
| 2023 | 12 | 0.002488 |
| 1918 | 7 | 0.001452 |
| 1917 | 6 | 0.001244 |
| 1916 | 5 | 0.001037 |
| 1902 | 3 | 0.000622 |
| 1904 | 3 | 0.000622 |
| 1915 | 3 | 0.000622 |
| 1903 | 2 | 0.000415 |
| 1908 | 2 | 0.000415 |
| 1911 | 2 | 0.000415 |
| 1906 | 2 | 0.000415 |
| 1907 | 2 | 0.000415 |
| 1900 | 2 | 0.000415 |
| 1901 | 1 | 0.000207 |
| 1912 | 1 | 0.000207 |
| 1910 | 1 | 0.000207 |
| 1905 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_entrycampaign__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_entrycampaign__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_entrycampaign__c es 43. Lo que supone un 0.008917017817445834%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mrCzQAI | 18002 | 3.733120 |
| 7013Y000001mrBSQAY | 17224 | 3.571784 |
| 7013Y000001mr2cQAA | 13558 | 2.811556 |
| 7013Y000001mr2DQAQ | 12301 | 2.550889 |
| 7013Y000001mr1MQAQ | 12285 | 2.547571 |
| ... | ... | ... |
| 7013Y000001vZryQAE | 1 | 0.000207 |
| 7013Y000001vZvlQAE | 1 | 0.000207 |
| 7013Y000001vaoyQAA | 1 | 0.000207 |
| 7013Y000001rAvqQAE | 1 | 0.000207 |
| 7013Y000001mrY3QAI | 1 | 0.000207 |
2634 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "leadsource"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable leadsource es 0. Lo que supone un 0.0% El nº de vacios para la variable leadsource es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Persona a persona | 139341 | 28.895493 |
| Otro | 115221 | 23.893668 |
| Telemarketing | 82413 | 17.090190 |
| Web MSF | 43532 | 9.027340 |
| Cupón | 37592 | 7.795547 |
| Personal con tablet | 31661 | 6.565621 |
| Teléfono campaña | 13900 | 2.882478 |
| Web terceros | 9419 | 1.953242 |
| Web campaña | 3324 | 0.689306 |
| Teléfono web | 2438 | 0.505574 |
| Teléfono SAS | 1346 | 0.279123 |
| Eventos | 842 | 0.174608 |
| Email a SAS | 718 | 0.148893 |
| Email a Empresas | 103 | 0.021359 |
| Email a Bodas | 102 | 0.021152 |
| Plataforma iniciativas | 100 | 0.020737 |
| Entidad financiera | 73 | 0.015138 |
| Correo postal sin cupón | 60 | 0.012442 |
| Teléfono Officers | 20 | 0.004147 |
| Teléfono Herencias y Legados | 3 | 0.000622 |
| Email herencias | 3 | 0.000622 |
| Email a One to one | 3 | 0.000622 |
| Email a officers Mid Donors | 2 | 0.000415 |
| Email a Iniciativas Solidarias | 2 | 0.000415 |
| Cloud page | 2 | 0.000415 |
| Email Director/a General | 1 | 0.000207 |
| SMS | 1 | 0.000207 |
| TelEfono officers | 1 | 0.000207 |
| Tel?fono SAS | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaigncolaborationchannel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstcampaigncolaborationchannel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaigncolaborationchannel__c es 11926. Lo que supone un 2.473124523043233%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Persona a persona | 135714 | 28.143352 |
| Telemarketing | 94523 | 19.601472 |
| Otro | 86148 | 17.864727 |
| Web MSF | 46585 | 9.660448 |
| Cupón | 39815 | 8.256536 |
| Personal con tablet | 31501 | 6.532441 |
| Teléfono campaña | 15706 | 3.256993 |
| 11926 | 2.473125 | |
| Web terceros | 8526 | 1.768058 |
| Web campaña | 3027 | 0.627717 |
| Teléfono web | 2647 | 0.548915 |
| Teléfono SAS | 2027 | 0.420344 |
| Email a SAS | 1169 | 0.242418 |
| Plataforma iniciativas | 869 | 0.180207 |
| Eventos | 573 | 0.118824 |
| web campaña | 453 | 0.093940 |
| Entidad financiera | 438 | 0.090829 |
| cupón | 133 | 0.027581 |
| Web MSF Mi perfil | 124 | 0.025714 |
| Email a Empresas | 96 | 0.019908 |
| Email a Bodas | 86 | 0.017834 |
| Correo postal sin cupón | 73 | 0.015138 |
| Teléfono Officers | 52 | 0.010783 |
| Email a officers Mid Donors | 4 | 0.000829 |
| Email a One to one | 3 | 0.000622 |
| Email a Iniciativas Solidarias | 3 | 0.000622 |
| Cloud page | 2 | 0.000415 |
| Email Director/a General | 1 | 0.000207 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_firstcampaigncolaborationchannel__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c', 'msf_lifetime__c', 'msf_firstcampaigncolaborationchannel__c']
# Vamos a realizar analisis por cada variable
var = "npo02__averageamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__averageamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 482224 | 100.0 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("npo02__averageamount__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c', 'msf_lifetime__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c']
# Vamos a realizar analisis por cada variable
var = "msf_isactivedonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Nunca | 301335 | 62.488595 |
| Exdonante | 132714 | 27.521235 |
| Donante | 48175 | 9.990171 |
# Vamos a realizar analisis por cada variable
var = "msf_isactiverecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Socio | 482224 | 100.0 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactiverecurringdonor__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datefirstrecurringdonorquota__c', 'msf_datelastrecurringdonorquota__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'msf_entrydatecurrentrecurringdonor__c', 'npsp__last_soft_credit_date__c', 'msf_firstentrydaterecurringdonor__c', 'npo02__firstclosedate__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'msf_ltvcont__c', 'mailingstate', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_firstcampaignentryrecurringdonor__c', 'msf_firstcampaingcolaboration__c', 'msf_firstannualizedquota__c', 'msf_recencydonorcont__c', 'msf_recencyrecurringdonorcont__c', 'msf_recencytotalcont__c', 'npo02__best_gift_year_total__c', 'msf_lastannualizedquota__c', 'msf_valuetotalcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c', 'msf_lifetime__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactiverecurringdonor__c']
# Vamos a realizar analisis por cada variable
var = "npsp__deceased__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__deceased__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 482136 | 99.981751 |
| True | 88 | 0.018249 |
# Vamos a realizar analisis por cada variable
var = "msf_begindatemsf__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2000-02-01 | 2211 | 0.458501 |
| 2000-01-01 | 1913 | 0.396704 |
| 2004-01-01 | 1749 | 0.362695 |
| 1995-02-01 | 1711 | 0.354814 |
| 1994-10-01 | 1686 | 0.349630 |
| ... | ... | ... |
| 1992-05-24 | 1 | 0.000207 |
| 1995-11-15 | 1 | 0.000207 |
| 1990-02-12 | 1 | 0.000207 |
| 1998-11-02 | 1 | 0.000207 |
| 2011-03-26 | 1 | 0.000207 |
9431 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_fechacambiolevelrelacion__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_fechacambiolevelrelacion__c es 4. Lo que supone un 0.0008294900295298452% El nº de vacios para la variable msf_fechacambiolevelrelacion__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-03-28 | 404097 | 83.799303 |
| 2020-07-20 | 4563 | 0.946249 |
| 2020-09-19 | 2005 | 0.415785 |
| 2022-01-02 | 1812 | 0.375762 |
| 2023-01-03 | 1058 | 0.219402 |
| 2020-09-20 | 766 | 0.158849 |
| 2021-01-04 | 604 | 0.125254 |
| 2022-01-15 | 578 | 0.119862 |
| 2023-01-02 | 563 | 0.116752 |
| 2022-03-22 | 412 | 0.085438 |
| 2022-05-06 | 362 | 0.075069 |
| 2020-09-25 | 358 | 0.074240 |
| 2022-05-11 | 346 | 0.071751 |
| 2020-09-22 | 342 | 0.070922 |
| 2023-02-10 | 324 | 0.067189 |
| 2022-06-04 | 315 | 0.065323 |
| 2022-10-21 | 310 | 0.064286 |
| 2022-12-03 | 309 | 0.064079 |
| 2020-12-04 | 301 | 0.062420 |
| 2020-09-23 | 290 | 0.060139 |
| 2022-11-24 | 284 | 0.058894 |
| 2023-02-09 | 272 | 0.056406 |
| 2022-12-23 | 270 | 0.055991 |
| 2020-09-24 | 268 | 0.055576 |
| 2022-12-04 | 257 | 0.053295 |
| 2022-03-05 | 254 | 0.052673 |
| 2021-06-18 | 253 | 0.052466 |
| 2023-01-26 | 252 | 0.052258 |
| 2022-03-11 | 249 | 0.051636 |
| 2021-02-05 | 238 | 0.049355 |
| 2023-02-12 | 236 | 0.048940 |
| 2022-03-12 | 230 | 0.047696 |
| 2021-03-04 | 230 | 0.047696 |
| 2023-02-17 | 226 | 0.046867 |
| 2021-07-08 | 225 | 0.046659 |
| 2021-03-11 | 224 | 0.046452 |
| 2023-02-21 | 222 | 0.046037 |
| 2021-01-03 | 219 | 0.045415 |
| 2021-12-03 | 217 | 0.045000 |
| 2022-03-09 | 215 | 0.044585 |
| 2022-03-10 | 214 | 0.044378 |
| 2023-02-23 | 213 | 0.044171 |
| 2022-06-17 | 212 | 0.043963 |
| 2023-02-15 | 210 | 0.043549 |
| 2022-02-05 | 206 | 0.042719 |
| 2023-05-26 | 206 | 0.042719 |
| 2023-07-05 | 205 | 0.042512 |
| 2022-12-20 | 204 | 0.042304 |
| 2023-03-30 | 203 | 0.042097 |
| 2021-01-28 | 198 | 0.041060 |
| 2020-11-20 | 197 | 0.040853 |
| 2023-04-05 | 196 | 0.040645 |
| 2023-06-16 | 188 | 0.038986 |
| 2022-11-18 | 188 | 0.038986 |
| 2022-09-23 | 185 | 0.038364 |
| 2021-06-25 | 183 | 0.037949 |
| 2022-12-15 | 180 | 0.037327 |
| 2022-03-17 | 180 | 0.037327 |
| 2021-05-20 | 180 | 0.037327 |
| 2020-11-27 | 179 | 0.037120 |
| 2023-02-08 | 176 | 0.036498 |
| 2020-11-06 | 175 | 0.036290 |
| 2023-05-11 | 175 | 0.036290 |
| 2021-05-13 | 175 | 0.036290 |
| 2021-11-18 | 174 | 0.036083 |
| 2023-06-22 | 171 | 0.035461 |
| 2023-06-09 | 168 | 0.034839 |
| 2022-07-07 | 167 | 0.034631 |
| 2023-06-29 | 167 | 0.034631 |
| 2022-09-28 | 166 | 0.034424 |
| 2023-02-16 | 165 | 0.034217 |
| 2023-06-21 | 165 | 0.034217 |
| 2022-03-16 | 165 | 0.034217 |
| 2023-06-30 | 164 | 0.034009 |
| 2023-07-07 | 164 | 0.034009 |
| 2021-08-07 | 164 | 0.034009 |
| 2021-02-11 | 162 | 0.033595 |
| 2021-01-21 | 162 | 0.033595 |
| 2022-03-04 | 161 | 0.033387 |
| 2023-06-28 | 161 | 0.033387 |
| 2022-09-08 | 161 | 0.033387 |
| 2022-11-30 | 160 | 0.033180 |
| 2022-03-15 | 159 | 0.032973 |
| 2023-07-06 | 159 | 0.032973 |
| 2022-03-24 | 158 | 0.032765 |
| 2021-03-18 | 157 | 0.032558 |
| 2023-05-25 | 156 | 0.032350 |
| 2022-05-19 | 156 | 0.032350 |
| 2020-10-03 | 155 | 0.032143 |
| 2023-05-12 | 155 | 0.032143 |
| 2022-12-16 | 155 | 0.032143 |
| 2023-02-24 | 155 | 0.032143 |
| 2021-05-08 | 155 | 0.032143 |
| 2021-05-27 | 155 | 0.032143 |
| 2021-02-18 | 153 | 0.031728 |
| 2022-10-06 | 153 | 0.031728 |
| 2022-01-06 | 152 | 0.031521 |
| 2022-05-12 | 152 | 0.031521 |
| 2021-03-06 | 151 | 0.031314 |
| 2023-03-23 | 151 | 0.031314 |
| 2022-03-08 | 150 | 0.031106 |
| 2023-02-18 | 150 | 0.031106 |
| 2020-10-24 | 150 | 0.031106 |
| 2023-06-15 | 150 | 0.031106 |
| 2023-06-23 | 148 | 0.030691 |
| 2022-03-18 | 148 | 0.030691 |
| 2023-05-24 | 148 | 0.030691 |
| 2023-01-19 | 148 | 0.030691 |
| 2023-06-17 | 148 | 0.030691 |
| 2021-04-29 | 147 | 0.030484 |
| 2023-03-10 | 146 | 0.030277 |
| 2023-03-17 | 146 | 0.030277 |
| 2022-11-10 | 145 | 0.030069 |
| 2022-11-17 | 144 | 0.029862 |
| 2023-04-14 | 144 | 0.029862 |
| 2021-12-22 | 143 | 0.029655 |
| 2021-04-22 | 143 | 0.029655 |
| 2021-11-07 | 142 | 0.029447 |
| 2023-06-01 | 142 | 0.029447 |
| 2021-04-17 | 142 | 0.029447 |
| 2023-06-06 | 142 | 0.029447 |
| 2022-01-28 | 141 | 0.029240 |
| 2023-03-16 | 141 | 0.029240 |
| 2022-11-11 | 140 | 0.029032 |
| 2022-11-23 | 140 | 0.029032 |
| 2020-10-22 | 140 | 0.029032 |
| 2021-02-25 | 140 | 0.029032 |
| 2021-06-10 | 139 | 0.028825 |
| 2023-04-19 | 138 | 0.028618 |
| 2021-06-05 | 138 | 0.028618 |
| 2021-04-01 | 138 | 0.028618 |
| 2022-11-25 | 138 | 0.028618 |
| 2023-04-21 | 138 | 0.028618 |
| 2022-09-30 | 137 | 0.028410 |
| 2022-12-10 | 137 | 0.028410 |
| 2023-04-26 | 137 | 0.028410 |
| 2023-06-08 | 135 | 0.027996 |
| 2022-10-04 | 135 | 0.027996 |
| 2023-04-28 | 134 | 0.027788 |
| 2023-07-08 | 134 | 0.027788 |
| 2021-04-15 | 133 | 0.027581 |
| 2023-01-04 | 132 | 0.027373 |
| 2023-06-24 | 132 | 0.027373 |
| 2022-03-31 | 132 | 0.027373 |
| 2023-02-11 | 132 | 0.027373 |
| 2020-11-17 | 131 | 0.027166 |
| 2020-10-29 | 131 | 0.027166 |
| 2022-05-28 | 131 | 0.027166 |
| 2023-05-18 | 131 | 0.027166 |
| 2022-03-07 | 130 | 0.026959 |
| 2022-12-21 | 130 | 0.026959 |
| 2023-05-31 | 129 | 0.026751 |
| 2023-02-01 | 129 | 0.026751 |
| 2021-03-25 | 128 | 0.026544 |
| 2021-11-13 | 128 | 0.026544 |
| 2022-11-16 | 127 | 0.026337 |
| 2022-11-09 | 127 | 0.026337 |
| 2021-12-23 | 126 | 0.026129 |
| 2023-01-12 | 126 | 0.026129 |
| 2023-01-21 | 125 | 0.025922 |
| 2020-12-25 | 125 | 0.025922 |
| 2020-12-30 | 124 | 0.025714 |
| 2023-05-27 | 124 | 0.025714 |
| 2023-05-09 | 124 | 0.025714 |
| 2021-10-03 | 123 | 0.025507 |
| 2021-11-25 | 123 | 0.025507 |
| 2022-03-25 | 123 | 0.025507 |
| 2023-03-24 | 122 | 0.025300 |
| 2023-06-03 | 122 | 0.025300 |
| 2021-09-09 | 121 | 0.025092 |
| 2021-10-04 | 120 | 0.024885 |
| 2023-02-28 | 120 | 0.024885 |
| 2022-10-20 | 120 | 0.024885 |
| 2023-03-31 | 120 | 0.024885 |
| 2022-07-22 | 119 | 0.024678 |
| 2021-07-29 | 119 | 0.024678 |
| 2022-10-27 | 119 | 0.024678 |
| 2022-10-14 | 118 | 0.024470 |
| 2023-01-06 | 118 | 0.024470 |
| 2021-06-09 | 117 | 0.024263 |
| 2023-02-25 | 117 | 0.024263 |
| 2021-02-10 | 117 | 0.024263 |
| 2023-04-07 | 117 | 0.024263 |
| 2021-02-17 | 116 | 0.024055 |
| 2022-11-26 | 116 | 0.024055 |
| 2023-06-14 | 116 | 0.024055 |
| 2021-06-03 | 116 | 0.024055 |
| 2022-04-28 | 115 | 0.023848 |
| 2022-02-03 | 114 | 0.023641 |
| 2023-04-27 | 114 | 0.023641 |
| 2020-12-14 | 113 | 0.023433 |
| 2021-07-22 | 113 | 0.023433 |
| 2022-12-29 | 113 | 0.023433 |
| 2021-12-17 | 112 | 0.023226 |
| 2021-10-29 | 112 | 0.023226 |
| 2021-01-06 | 112 | 0.023226 |
| 2023-05-19 | 112 | 0.023226 |
| 2022-11-22 | 112 | 0.023226 |
| 2022-11-01 | 111 | 0.023019 |
| 2021-01-14 | 111 | 0.023019 |
| 2021-09-04 | 111 | 0.023019 |
| 2020-10-06 | 111 | 0.023019 |
| 2023-05-07 | 111 | 0.023019 |
| 2022-06-23 | 111 | 0.023019 |
| 2023-06-20 | 110 | 0.022811 |
| 2023-02-07 | 110 | 0.022811 |
| 2021-11-11 | 110 | 0.022811 |
| 2021-07-03 | 110 | 0.022811 |
| 2020-09-29 | 109 | 0.022604 |
| 2020-12-15 | 109 | 0.022604 |
| 2021-07-01 | 109 | 0.022604 |
| 2022-10-28 | 108 | 0.022396 |
| 2022-04-14 | 108 | 0.022396 |
| 2023-02-14 | 108 | 0.022396 |
| 2022-11-08 | 107 | 0.022189 |
| 2023-01-27 | 107 | 0.022189 |
| 2022-05-13 | 107 | 0.022189 |
| 2021-12-14 | 107 | 0.022189 |
| 2023-02-05 | 106 | 0.021982 |
| 2022-12-17 | 106 | 0.021982 |
| 2021-02-24 | 106 | 0.021982 |
| 2022-06-10 | 106 | 0.021982 |
| 2023-03-01 | 106 | 0.021982 |
| 2022-07-14 | 105 | 0.021774 |
| 2020-10-15 | 104 | 0.021567 |
| 2023-07-01 | 104 | 0.021567 |
| 2020-10-20 | 103 | 0.021360 |
| 2023-05-13 | 103 | 0.021360 |
| 2022-09-04 | 103 | 0.021360 |
| 2022-06-29 | 103 | 0.021360 |
| 2020-12-19 | 103 | 0.021360 |
| 2022-07-13 | 102 | 0.021152 |
| 2022-04-09 | 102 | 0.021152 |
| 2023-04-25 | 101 | 0.020945 |
| 2023-06-27 | 101 | 0.020945 |
| 2023-05-30 | 100 | 0.020737 |
| 2021-09-23 | 100 | 0.020737 |
| 2022-07-21 | 99 | 0.020530 |
| 2021-03-27 | 99 | 0.020530 |
| 2023-05-17 | 98 | 0.020323 |
| 2023-01-25 | 98 | 0.020323 |
| 2022-02-10 | 98 | 0.020323 |
| 2022-07-15 | 98 | 0.020323 |
| 2022-03-23 | 98 | 0.020323 |
| 2022-10-26 | 98 | 0.020323 |
| 2022-09-15 | 97 | 0.020115 |
| 2022-04-22 | 97 | 0.020115 |
| 2021-10-16 | 97 | 0.020115 |
| 2022-12-01 | 97 | 0.020115 |
| 2021-05-06 | 96 | 0.019908 |
| 2023-05-16 | 96 | 0.019908 |
| 2022-04-29 | 96 | 0.019908 |
| 2022-02-18 | 96 | 0.019908 |
| 2023-04-22 | 96 | 0.019908 |
| 2023-01-20 | 95 | 0.019701 |
| 2021-06-29 | 95 | 0.019701 |
| 2023-05-23 | 95 | 0.019701 |
| 2020-12-11 | 95 | 0.019701 |
| 2023-03-08 | 94 | 0.019493 |
| 2023-04-20 | 94 | 0.019493 |
| 2021-07-15 | 94 | 0.019493 |
| 2021-04-16 | 94 | 0.019493 |
| 2021-04-08 | 93 | 0.019286 |
| 2022-05-14 | 93 | 0.019286 |
| 2022-01-19 | 93 | 0.019286 |
| 2022-04-08 | 93 | 0.019286 |
| 2022-05-10 | 93 | 0.019286 |
| 2020-12-03 | 93 | 0.019286 |
| 2023-02-03 | 93 | 0.019286 |
| 2023-03-26 | 93 | 0.019286 |
| 2022-11-29 | 92 | 0.019078 |
| 2022-03-03 | 92 | 0.019078 |
| 2022-11-15 | 92 | 0.019078 |
| 2021-10-01 | 91 | 0.018871 |
| 2023-01-31 | 91 | 0.018871 |
| 2023-05-06 | 91 | 0.018871 |
| 2021-05-29 | 91 | 0.018871 |
| 2023-06-13 | 91 | 0.018871 |
| 2022-01-10 | 91 | 0.018871 |
| 2023-03-14 | 91 | 0.018871 |
| 2022-12-14 | 90 | 0.018664 |
| 2022-02-11 | 90 | 0.018664 |
| 2022-04-12 | 90 | 0.018664 |
| 2022-03-26 | 90 | 0.018664 |
| 2020-10-10 | 90 | 0.018664 |
| 2021-12-21 | 90 | 0.018664 |
| 2021-04-23 | 90 | 0.018664 |
| 2023-03-03 | 89 | 0.018456 |
| 2021-10-22 | 89 | 0.018456 |
| 2022-08-06 | 89 | 0.018456 |
| 2022-05-20 | 89 | 0.018456 |
| 2021-12-29 | 89 | 0.018456 |
| 2022-03-30 | 89 | 0.018456 |
| 2023-01-13 | 89 | 0.018456 |
| 2021-11-19 | 89 | 0.018456 |
| 2022-04-01 | 88 | 0.018249 |
| 2022-11-12 | 88 | 0.018249 |
| 2023-03-05 | 88 | 0.018249 |
| 2023-05-20 | 88 | 0.018249 |
| 2021-10-08 | 87 | 0.018042 |
| 2022-10-19 | 87 | 0.018042 |
| 2022-12-30 | 87 | 0.018042 |
| 2022-06-15 | 87 | 0.018042 |
| 2020-11-28 | 86 | 0.017834 |
| 2022-07-10 | 86 | 0.017834 |
| 2023-01-14 | 86 | 0.017834 |
| 2020-10-14 | 86 | 0.017834 |
| 2023-01-28 | 86 | 0.017834 |
| 2023-03-22 | 85 | 0.017627 |
| 2022-05-31 | 85 | 0.017627 |
| 2022-11-19 | 85 | 0.017627 |
| 2021-12-19 | 85 | 0.017627 |
| 2021-02-26 | 85 | 0.017627 |
| 2022-10-25 | 85 | 0.017627 |
| 2020-10-30 | 85 | 0.017627 |
| 2022-06-08 | 85 | 0.017627 |
| 2023-03-21 | 84 | 0.017419 |
| 2022-02-26 | 84 | 0.017419 |
| 2022-07-28 | 84 | 0.017419 |
| 2023-03-18 | 84 | 0.017419 |
| 2021-11-30 | 84 | 0.017419 |
| 2022-01-27 | 84 | 0.017419 |
| 2022-03-19 | 84 | 0.017419 |
| 2022-09-17 | 84 | 0.017419 |
| 2022-02-23 | 83 | 0.017212 |
| 2022-04-07 | 83 | 0.017212 |
| 2022-03-01 | 83 | 0.017212 |
| 2021-01-20 | 83 | 0.017212 |
| 2022-01-20 | 83 | 0.017212 |
| 2022-10-15 | 82 | 0.017005 |
| 2023-04-12 | 82 | 0.017005 |
| 2021-10-15 | 82 | 0.017005 |
| 2022-01-21 | 82 | 0.017005 |
| 2021-10-23 | 82 | 0.017005 |
| 2022-10-22 | 82 | 0.017005 |
| 2021-11-24 | 82 | 0.017005 |
| 2022-02-16 | 82 | 0.017005 |
| 2023-04-13 | 82 | 0.017005 |
| 2022-02-24 | 82 | 0.017005 |
| 2021-11-27 | 82 | 0.017005 |
| 2022-02-17 | 82 | 0.017005 |
| 2023-03-09 | 81 | 0.016797 |
| 2022-10-12 | 81 | 0.016797 |
| 2022-06-30 | 81 | 0.016797 |
| 2023-06-10 | 81 | 0.016797 |
| 2021-06-04 | 81 | 0.016797 |
| 2023-01-17 | 81 | 0.016797 |
| 2022-01-08 | 80 | 0.016590 |
| 2020-12-29 | 80 | 0.016590 |
| 2023-05-10 | 80 | 0.016590 |
| 2021-09-16 | 79 | 0.016383 |
| 2022-11-03 | 79 | 0.016383 |
| 2021-12-24 | 79 | 0.016383 |
| 2022-07-01 | 79 | 0.016383 |
| 2021-12-30 | 79 | 0.016383 |
| 2021-12-01 | 79 | 0.016383 |
| 2022-10-07 | 78 | 0.016175 |
| 2022-11-06 | 78 | 0.016175 |
| 2022-05-27 | 78 | 0.016175 |
| 2023-03-11 | 78 | 0.016175 |
| 2022-10-18 | 78 | 0.016175 |
| 2021-05-16 | 77 | 0.015968 |
| 2022-06-24 | 77 | 0.015968 |
| 2022-10-09 | 77 | 0.015968 |
| 2023-05-04 | 77 | 0.015968 |
| 2022-06-11 | 77 | 0.015968 |
| 2023-03-07 | 77 | 0.015968 |
| 2021-09-22 | 77 | 0.015968 |
| 2022-08-11 | 76 | 0.015760 |
| 2022-07-20 | 76 | 0.015760 |
| 2023-04-01 | 76 | 0.015760 |
| 2022-07-08 | 76 | 0.015760 |
| 2023-01-05 | 76 | 0.015760 |
| 2022-12-08 | 76 | 0.015760 |
| 2022-04-15 | 76 | 0.015760 |
| 2021-12-11 | 75 | 0.015553 |
| 2021-11-06 | 75 | 0.015553 |
| 2022-12-13 | 75 | 0.015553 |
| 2023-03-15 | 75 | 0.015553 |
| 2023-01-10 | 74 | 0.015346 |
| 2022-06-01 | 74 | 0.015346 |
| 2020-12-22 | 74 | 0.015346 |
| 2021-04-30 | 73 | 0.015138 |
| 2021-12-16 | 73 | 0.015138 |
| 2022-07-16 | 73 | 0.015138 |
| 2021-05-28 | 73 | 0.015138 |
| 2022-09-29 | 72 | 0.014931 |
| 2020-11-13 | 72 | 0.014931 |
| 2022-03-29 | 72 | 0.014931 |
| 2022-09-16 | 72 | 0.014931 |
| 2022-10-03 | 72 | 0.014931 |
| 2021-04-21 | 71 | 0.014724 |
| 2021-07-14 | 71 | 0.014724 |
| 2021-11-16 | 71 | 0.014724 |
| 2022-12-31 | 71 | 0.014724 |
| 2022-01-12 | 71 | 0.014724 |
| 2023-01-18 | 71 | 0.014724 |
| 2023-01-24 | 71 | 0.014724 |
| 2022-11-05 | 71 | 0.014724 |
| 2022-12-28 | 71 | 0.014724 |
| 2022-04-13 | 71 | 0.014724 |
| 2021-09-17 | 71 | 0.014724 |
| 2021-10-19 | 71 | 0.014724 |
| 2021-12-10 | 71 | 0.014724 |
| 2022-10-29 | 71 | 0.014724 |
| 2021-07-09 | 70 | 0.014516 |
| 2021-01-08 | 70 | 0.014516 |
| 2020-10-16 | 70 | 0.014516 |
| 2021-06-12 | 70 | 0.014516 |
| 2021-07-13 | 70 | 0.014516 |
| 2020-12-17 | 70 | 0.014516 |
| 2020-12-24 | 70 | 0.014516 |
| 2021-10-21 | 70 | 0.014516 |
| 2021-12-08 | 69 | 0.014309 |
| 2021-11-10 | 69 | 0.014309 |
| 2023-03-28 | 69 | 0.014309 |
| 2023-06-07 | 69 | 0.014309 |
| 2023-05-03 | 69 | 0.014309 |
| 2021-09-10 | 69 | 0.014309 |
| 2022-12-24 | 69 | 0.014309 |
| 2022-06-16 | 69 | 0.014309 |
| 2022-09-14 | 68 | 0.014101 |
| 2022-01-13 | 68 | 0.014101 |
| 2022-10-11 | 68 | 0.014101 |
| 2021-02-04 | 68 | 0.014101 |
| 2020-12-08 | 68 | 0.014101 |
| 2021-09-30 | 68 | 0.014101 |
| 2021-10-27 | 68 | 0.014101 |
| 2021-06-30 | 68 | 0.014101 |
| 2021-07-31 | 67 | 0.013894 |
| 2021-04-09 | 67 | 0.013894 |
| 2022-12-06 | 67 | 0.013894 |
| 2021-05-22 | 67 | 0.013894 |
| 2020-11-10 | 67 | 0.013894 |
| 2022-06-22 | 67 | 0.013894 |
| 2022-05-24 | 67 | 0.013894 |
| 2021-10-07 | 67 | 0.013894 |
| 2023-04-18 | 66 | 0.013687 |
| 2023-04-15 | 66 | 0.013687 |
| 2021-08-11 | 66 | 0.013687 |
| 2020-12-23 | 66 | 0.013687 |
| 2022-05-08 | 66 | 0.013687 |
| 2022-07-12 | 66 | 0.013687 |
| 2023-01-11 | 66 | 0.013687 |
| 2021-12-15 | 66 | 0.013687 |
| 2022-04-03 | 66 | 0.013687 |
| 2021-02-03 | 66 | 0.013687 |
| 2021-12-04 | 66 | 0.013687 |
| 2021-03-31 | 66 | 0.013687 |
| 2022-02-12 | 66 | 0.013687 |
| 2022-05-21 | 66 | 0.013687 |
| 2021-01-16 | 65 | 0.013479 |
| 2020-11-24 | 65 | 0.013479 |
| 2022-07-29 | 65 | 0.013479 |
| 2022-10-01 | 64 | 0.013272 |
| 2021-09-29 | 64 | 0.013272 |
| 2021-07-10 | 64 | 0.013272 |
| 2021-09-24 | 64 | 0.013272 |
| 2022-08-04 | 64 | 0.013272 |
| 2021-06-19 | 64 | 0.013272 |
| 2022-08-05 | 64 | 0.013272 |
| 2021-09-15 | 64 | 0.013272 |
| 2021-05-21 | 64 | 0.013272 |
| 2022-09-09 | 64 | 0.013272 |
| 2023-04-29 | 63 | 0.013065 |
| 2022-03-14 | 63 | 0.013065 |
| 2021-02-20 | 63 | 0.013065 |
| 2020-10-17 | 63 | 0.013065 |
| 2020-10-28 | 63 | 0.013065 |
| 2021-12-05 | 63 | 0.013065 |
| 2021-06-01 | 63 | 0.013065 |
| 2022-05-17 | 63 | 0.013065 |
| 2020-12-18 | 63 | 0.013065 |
| 2022-05-03 | 63 | 0.013065 |
| 2021-04-28 | 63 | 0.013065 |
| 2021-10-09 | 63 | 0.013065 |
| 2022-07-23 | 63 | 0.013065 |
| 2021-01-29 | 63 | 0.013065 |
| 2021-02-27 | 63 | 0.013065 |
| 2022-04-27 | 63 | 0.013065 |
| 2021-05-15 | 62 | 0.012857 |
| 2021-07-16 | 62 | 0.012857 |
| 2021-01-09 | 62 | 0.012857 |
| 2022-03-20 | 62 | 0.012857 |
| 2021-06-11 | 62 | 0.012857 |
| 2020-11-14 | 61 | 0.012650 |
| 2022-02-08 | 61 | 0.012650 |
| 2022-08-10 | 61 | 0.012650 |
| 2022-02-22 | 61 | 0.012650 |
| 2021-02-06 | 61 | 0.012650 |
| 2022-01-18 | 61 | 0.012650 |
| 2020-10-01 | 61 | 0.012650 |
| 2022-06-09 | 61 | 0.012650 |
| 2020-12-12 | 61 | 0.012650 |
| 2021-09-28 | 61 | 0.012650 |
| 2022-07-30 | 61 | 0.012650 |
| 2022-06-14 | 60 | 0.012442 |
| 2020-11-19 | 60 | 0.012442 |
| 2021-09-03 | 60 | 0.012442 |
| 2020-12-16 | 60 | 0.012442 |
| 2020-09-21 | 60 | 0.012442 |
| 2021-11-23 | 59 | 0.012235 |
| 2021-07-28 | 59 | 0.012235 |
| 2021-05-26 | 58 | 0.012028 |
| 2021-02-16 | 58 | 0.012028 |
| 2022-02-01 | 58 | 0.012028 |
| 2021-12-31 | 58 | 0.012028 |
| 2021-04-27 | 58 | 0.012028 |
| 2021-10-20 | 58 | 0.012028 |
| 2021-01-15 | 58 | 0.012028 |
| 2021-02-23 | 58 | 0.012028 |
| 2022-09-27 | 58 | 0.012028 |
| 2020-11-04 | 57 | 0.011820 |
| 2022-09-24 | 57 | 0.011820 |
| 2022-05-26 | 57 | 0.011820 |
| 2021-07-30 | 57 | 0.011820 |
| 2022-03-13 | 57 | 0.011820 |
| 2021-01-26 | 57 | 0.011820 |
| 2022-07-27 | 56 | 0.011613 |
| 2021-08-04 | 56 | 0.011613 |
| 2021-11-20 | 56 | 0.011613 |
| 2020-11-25 | 56 | 0.011613 |
| 2021-08-10 | 56 | 0.011613 |
| 2021-10-30 | 56 | 0.011613 |
| 2022-02-09 | 56 | 0.011613 |
| 2021-09-18 | 56 | 0.011613 |
| 2022-07-03 | 56 | 0.011613 |
| 2021-12-28 | 56 | 0.011613 |
| 2021-09-25 | 55 | 0.011406 |
| 2021-03-30 | 55 | 0.011406 |
| 2022-05-04 | 55 | 0.011406 |
| 2022-04-21 | 55 | 0.011406 |
| 2020-07-16 | 55 | 0.011406 |
| 2020-11-12 | 55 | 0.011406 |
| 2021-10-12 | 55 | 0.011406 |
| 2021-10-05 | 55 | 0.011406 |
| 2021-05-11 | 54 | 0.011198 |
| 2022-09-10 | 54 | 0.011198 |
| 2021-06-23 | 54 | 0.011198 |
| 2021-06-15 | 54 | 0.011198 |
| 2021-01-31 | 54 | 0.011198 |
| 2021-03-26 | 54 | 0.011198 |
| 2021-01-01 | 54 | 0.011198 |
| 2021-03-12 | 54 | 0.011198 |
| 2022-01-22 | 54 | 0.011198 |
| 2021-05-05 | 53 | 0.010991 |
| 2020-10-27 | 53 | 0.010991 |
| 2021-01-22 | 53 | 0.010991 |
| 2022-02-19 | 53 | 0.010991 |
| 2021-05-01 | 53 | 0.010991 |
| 2021-03-13 | 53 | 0.010991 |
| 2021-01-19 | 53 | 0.010991 |
| 2022-01-26 | 52 | 0.010783 |
| 2021-07-23 | 52 | 0.010783 |
| 2021-01-23 | 52 | 0.010783 |
| 2020-11-18 | 52 | 0.010783 |
| 2021-09-08 | 52 | 0.010783 |
| 2020-10-23 | 52 | 0.010783 |
| 2022-06-03 | 52 | 0.010783 |
| 2021-10-26 | 52 | 0.010783 |
| 2022-01-29 | 52 | 0.010783 |
| 2021-10-06 | 52 | 0.010783 |
| 2021-03-17 | 51 | 0.010576 |
| 2022-04-30 | 51 | 0.010576 |
| 2022-04-20 | 51 | 0.010576 |
| 2022-06-25 | 51 | 0.010576 |
| 2022-02-25 | 51 | 0.010576 |
| 2022-04-23 | 51 | 0.010576 |
| 2021-04-24 | 51 | 0.010576 |
| 2022-01-25 | 51 | 0.010576 |
| 2021-06-24 | 51 | 0.010576 |
| 2020-12-01 | 51 | 0.010576 |
| 2021-06-26 | 51 | 0.010576 |
| 2021-03-19 | 51 | 0.010576 |
| 2021-08-18 | 50 | 0.010369 |
| 2021-05-25 | 50 | 0.010369 |
| 2021-05-04 | 50 | 0.010369 |
| 2022-09-22 | 50 | 0.010369 |
| 2021-09-07 | 50 | 0.010369 |
| 2021-09-11 | 50 | 0.010369 |
| 2021-03-24 | 50 | 0.010369 |
| 2020-11-26 | 50 | 0.010369 |
| 2020-12-21 | 49 | 0.010161 |
| 2021-04-20 | 49 | 0.010161 |
| 2020-09-27 | 49 | 0.010161 |
| 2021-04-07 | 49 | 0.010161 |
| 2020-11-11 | 49 | 0.010161 |
| 2022-06-21 | 49 | 0.010161 |
| 2021-10-28 | 48 | 0.009954 |
| 2022-06-18 | 48 | 0.009954 |
| 2022-01-01 | 48 | 0.009954 |
| 2020-12-31 | 48 | 0.009954 |
| 2022-08-03 | 48 | 0.009954 |
| 2022-02-15 | 48 | 0.009954 |
| 2020-11-21 | 48 | 0.009954 |
| 2021-02-13 | 48 | 0.009954 |
| 2021-12-25 | 47 | 0.009747 |
| 2021-11-09 | 47 | 0.009747 |
| 2022-06-07 | 47 | 0.009747 |
| 2021-04-14 | 47 | 0.009747 |
| 2021-04-13 | 47 | 0.009747 |
| 2021-05-18 | 46 | 0.009539 |
| 2022-08-12 | 46 | 0.009539 |
| 2022-04-05 | 46 | 0.009539 |
| 2021-08-06 | 46 | 0.009539 |
| 2021-06-16 | 45 | 0.009332 |
| 2021-05-19 | 45 | 0.009332 |
| 2021-02-12 | 45 | 0.009332 |
| 2023-03-04 | 45 | 0.009332 |
| 2021-07-21 | 45 | 0.009332 |
| 2022-07-26 | 45 | 0.009332 |
| 2022-04-26 | 44 | 0.009124 |
| 2022-01-14 | 44 | 0.009124 |
| 2022-01-30 | 44 | 0.009124 |
| 2021-11-26 | 44 | 0.009124 |
| 2022-07-19 | 44 | 0.009124 |
| 2022-09-20 | 44 | 0.009124 |
| 2021-02-09 | 44 | 0.009124 |
| 2020-10-25 | 43 | 0.008917 |
| 2022-06-28 | 43 | 0.008917 |
| 2020-10-31 | 42 | 0.008710 |
| 2021-07-17 | 42 | 0.008710 |
| 2021-01-27 | 42 | 0.008710 |
| 2020-12-05 | 41 | 0.008502 |
| 2021-04-10 | 41 | 0.008502 |
| 2022-08-17 | 41 | 0.008502 |
| 2020-10-21 | 39 | 0.008088 |
| 2020-10-08 | 39 | 0.008088 |
| 2021-07-27 | 39 | 0.008088 |
| 2021-03-16 | 39 | 0.008088 |
| 2023-02-13 | 39 | 0.008088 |
| 2021-07-20 | 38 | 0.007880 |
| 2020-10-09 | 38 | 0.007880 |
| 2021-01-13 | 37 | 0.007673 |
| 2021-08-19 | 37 | 0.007673 |
| 2021-09-01 | 37 | 0.007673 |
| 2021-01-07 | 37 | 0.007673 |
| 2021-07-24 | 37 | 0.007673 |
| 2022-08-24 | 37 | 0.007673 |
| 2022-12-27 | 36 | 0.007465 |
| 2020-11-05 | 36 | 0.007465 |
| 2021-11-03 | 36 | 0.007465 |
| 2022-08-31 | 36 | 0.007465 |
| 2021-10-14 | 35 | 0.007258 |
| 2022-01-11 | 35 | 0.007258 |
| 2021-09-14 | 35 | 0.007258 |
| 2022-09-13 | 35 | 0.007258 |
| 2020-10-04 | 35 | 0.007258 |
| 2021-02-19 | 35 | 0.007258 |
| 2023-06-04 | 34 | 0.007051 |
| 2021-07-07 | 34 | 0.007051 |
| 2021-01-12 | 34 | 0.007051 |
| 2022-08-13 | 33 | 0.006843 |
| 2021-03-01 | 33 | 0.006843 |
| 2021-03-23 | 33 | 0.006843 |
| 2023-04-11 | 33 | 0.006843 |
| 2021-08-25 | 33 | 0.006843 |
| 2021-03-10 | 32 | 0.006636 |
| 2020-09-26 | 32 | 0.006636 |
| 2020-12-06 | 32 | 0.006636 |
| 2021-08-26 | 30 | 0.006221 |
| 2021-03-09 | 29 | 0.006014 |
| 2020-09-15 | 29 | 0.006014 |
| 2021-03-20 | 28 | 0.005806 |
| 2021-09-21 | 27 | 0.005599 |
| 2022-08-09 | 27 | 0.005599 |
| 2021-08-13 | 26 | 0.005392 |
| 2022-05-25 | 26 | 0.005392 |
| 2021-08-31 | 26 | 0.005392 |
| 2020-11-08 | 26 | 0.005392 |
| 2021-08-17 | 25 | 0.005184 |
| 2022-02-28 | 25 | 0.005184 |
| 2022-04-19 | 25 | 0.005184 |
| 2020-10-05 | 25 | 0.005184 |
| 2021-08-01 | 25 | 0.005184 |
| 2021-11-04 | 24 | 0.004977 |
| 2021-08-12 | 24 | 0.004977 |
| 2020-12-10 | 24 | 0.004977 |
| 2023-06-25 | 24 | 0.004977 |
| 2021-03-03 | 23 | 0.004770 |
| 2021-03-05 | 22 | 0.004562 |
| 2022-01-03 | 21 | 0.004355 |
| 2022-11-04 | 21 | 0.004355 |
| 2021-02-01 | 20 | 0.004147 |
| 2021-04-03 | 20 | 0.004147 |
| 2021-08-14 | 20 | 0.004147 |
| 2021-01-05 | 20 | 0.004147 |
| 2021-08-20 | 20 | 0.004147 |
| 2022-03-21 | 19 | 0.003940 |
| 2022-09-21 | 19 | 0.003940 |
| 2022-07-05 | 18 | 0.003733 |
| 2021-12-26 | 18 | 0.003733 |
| 2020-12-20 | 18 | 0.003733 |
| 2021-08-24 | 18 | 0.003733 |
| 2021-08-21 | 17 | 0.003525 |
| 2021-11-05 | 17 | 0.003525 |
| 2022-03-28 | 17 | 0.003525 |
| 2022-09-01 | 17 | 0.003525 |
| 2021-08-28 | 17 | 0.003525 |
| 2023-03-29 | 16 | 0.003318 |
| 2020-12-09 | 16 | 0.003318 |
| 2023-02-20 | 16 | 0.003318 |
| 2021-04-06 | 16 | 0.003318 |
| 2022-02-07 | 15 | 0.003111 |
| 2023-01-01 | 15 | 0.003111 |
| 2023-02-19 | 15 | 0.003111 |
| 2021-12-20 | 15 | 0.003111 |
| 2021-02-22 | 14 | 0.002903 |
| 2022-11-27 | 14 | 0.002903 |
| 2022-08-27 | 14 | 0.002903 |
| 2022-01-24 | 13 | 0.002696 |
| 2021-08-27 | 13 | 0.002696 |
| 2022-12-19 | 13 | 0.002696 |
| 2021-12-07 | 13 | 0.002696 |
| 2021-01-11 | 12 | 0.002488 |
| 2020-11-09 | 12 | 0.002488 |
| 2022-02-27 | 12 | 0.002488 |
| 2021-01-24 | 11 | 0.002281 |
| 2022-10-05 | 11 | 0.002281 |
| 2021-12-27 | 11 | 0.002281 |
| 2022-08-18 | 11 | 0.002281 |
| 2022-07-06 | 11 | 0.002281 |
| 2021-02-07 | 11 | 0.002281 |
| 2020-04-11 | 11 | 0.002281 |
| 2023-02-04 | 11 | 0.002281 |
| 2022-08-25 | 11 | 0.002281 |
| 2021-04-26 | 11 | 0.002281 |
| 2022-01-17 | 11 | 0.002281 |
| 2021-04-04 | 10 | 0.002074 |
| 2022-01-31 | 10 | 0.002074 |
| 2020-11-03 | 10 | 0.002074 |
| 2022-12-18 | 10 | 0.002074 |
| 2022-12-05 | 10 | 0.002074 |
| 2021-08-09 | 10 | 0.002074 |
| 2021-02-08 | 10 | 0.002074 |
| 2020-11-23 | 10 | 0.002074 |
| 2022-08-19 | 10 | 0.002074 |
| 2022-09-05 | 10 | 0.002074 |
| 2022-04-11 | 9 | 0.001866 |
| 2022-10-31 | 9 | 0.001866 |
| 2021-01-10 | 9 | 0.001866 |
| 2020-12-07 | 9 | 0.001866 |
| 2022-12-26 | 9 | 0.001866 |
| 2021-01-25 | 9 | 0.001866 |
| 2020-11-01 | 9 | 0.001866 |
| 2020-11-30 | 9 | 0.001866 |
| 2022-11-21 | 9 | 0.001866 |
| 2022-11-20 | 9 | 0.001866 |
| 2020-11-16 | 9 | 0.001866 |
| 2020-12-28 | 9 | 0.001866 |
| 2020-10-26 | 9 | 0.001866 |
| 2021-06-20 | 9 | 0.001866 |
| 2021-12-12 | 8 | 0.001659 |
| 2021-08-16 | 8 | 0.001659 |
| 2023-05-02 | 8 | 0.001659 |
| 2021-08-22 | 8 | 0.001659 |
| 2021-01-17 | 8 | 0.001659 |
| 2022-07-17 | 8 | 0.001659 |
| 2021-05-10 | 8 | 0.001659 |
| 2020-12-26 | 8 | 0.001659 |
| 2022-01-23 | 8 | 0.001659 |
| 2022-02-13 | 8 | 0.001659 |
| 2023-01-30 | 8 | 0.001659 |
| 2020-12-27 | 8 | 0.001659 |
| 2023-06-12 | 8 | 0.001659 |
| 2021-02-15 | 8 | 0.001659 |
| 2022-02-04 | 8 | 0.001659 |
| 2022-12-25 | 8 | 0.001659 |
| 2020-10-18 | 8 | 0.001659 |
| 2022-04-16 | 8 | 0.001659 |
| 2021-03-29 | 8 | 0.001659 |
| 2021-05-24 | 7 | 0.001452 |
| 2020-12-13 | 7 | 0.001452 |
| 2021-05-03 | 7 | 0.001452 |
| 2021-11-29 | 7 | 0.001452 |
| 2022-12-11 | 7 | 0.001452 |
| 2021-06-06 | 7 | 0.001452 |
| 2021-08-23 | 7 | 0.001452 |
| 2022-08-16 | 7 | 0.001452 |
| 2022-05-15 | 7 | 0.001452 |
| 2023-04-08 | 7 | 0.001452 |
| 2022-08-26 | 7 | 0.001452 |
| 2022-06-19 | 7 | 0.001452 |
| 2021-06-27 | 7 | 0.001452 |
| 2020-11-22 | 7 | 0.001452 |
| 2021-06-07 | 7 | 0.001452 |
| 2023-04-30 | 7 | 0.001452 |
| 2022-05-05 | 7 | 0.001452 |
| 2021-12-09 | 7 | 0.001452 |
| 2022-08-23 | 7 | 0.001452 |
| 2023-01-08 | 7 | 0.001452 |
| 2021-03-08 | 7 | 0.001452 |
| 2021-11-15 | 7 | 0.001452 |
| 2023-01-23 | 7 | 0.001452 |
| 2021-02-14 | 7 | 0.001452 |
| 2021-04-18 | 7 | 0.001452 |
| 2022-12-07 | 7 | 0.001452 |
| 2021-04-25 | 7 | 0.001452 |
| 2021-03-15 | 7 | 0.001452 |
| 2022-05-01 | 6 | 0.001244 |
| 2021-07-04 | 6 | 0.001244 |
| 2021-11-01 | 6 | 0.001244 |
| 2022-06-06 | 6 | 0.001244 |
| 2023-03-13 | 6 | 0.001244 |
| 2023-01-15 | 6 | 0.001244 |
| 2021-04-11 | 6 | 0.001244 |
| 2021-06-14 | 6 | 0.001244 |
| 2021-04-12 | 6 | 0.001244 |
| 2021-09-13 | 6 | 0.001244 |
| 2021-02-21 | 6 | 0.001244 |
| 2022-04-04 | 6 | 0.001244 |
| 2021-10-31 | 6 | 0.001244 |
| 2020-11-29 | 6 | 0.001244 |
| 2022-05-16 | 6 | 0.001244 |
| 2021-01-30 | 6 | 0.001244 |
| 2021-04-05 | 6 | 0.001244 |
| 2022-01-16 | 6 | 0.001244 |
| 2021-02-28 | 6 | 0.001244 |
| 2022-02-21 | 6 | 0.001244 |
| 2023-02-27 | 6 | 0.001244 |
| 2021-05-31 | 6 | 0.001244 |
| 2021-01-18 | 6 | 0.001244 |
| 2022-10-30 | 6 | 0.001244 |
| 2022-11-13 | 6 | 0.001244 |
| 2021-04-19 | 6 | 0.001244 |
| 2020-10-07 | 6 | 0.001244 |
| 2021-08-08 | 6 | 0.001244 |
| 2023-07-09 | 6 | 0.001244 |
| 2022-08-30 | 6 | 0.001244 |
| 2023-06-05 | 5 | 0.001037 |
| 2023-04-17 | 5 | 0.001037 |
| 2021-12-13 | 5 | 0.001037 |
| 2021-06-21 | 5 | 0.001037 |
| 2023-01-29 | 5 | 0.001037 |
| 2021-08-15 | 5 | 0.001037 |
| 2021-03-22 | 5 | 0.001037 |
| 2023-02-26 | 5 | 0.001037 |
| 2022-05-23 | 5 | 0.001037 |
| 2022-12-12 | 5 | 0.001037 |
| 2020-10-13 | 5 | 0.001037 |
| 2021-03-07 | 5 | 0.001037 |
| 2022-08-08 | 5 | 0.001037 |
| 2022-12-09 | 5 | 0.001037 |
| 2021-10-10 | 5 | 0.001037 |
| 2023-04-23 | 5 | 0.001037 |
| 2021-09-12 | 5 | 0.001037 |
| 2023-04-03 | 5 | 0.001037 |
| 2022-06-13 | 5 | 0.001037 |
| 2022-11-07 | 5 | 0.001037 |
| 2023-04-09 | 5 | 0.001037 |
| 2021-10-17 | 5 | 0.001037 |
| 2021-10-13 | 5 | 0.001037 |
| 2023-06-19 | 5 | 0.001037 |
| 2023-01-09 | 5 | 0.001037 |
| 2022-07-18 | 5 | 0.001037 |
| 2023-05-22 | 5 | 0.001037 |
| 2022-05-09 | 4 | 0.000829 |
| 2022-02-20 | 4 | 0.000829 |
| 2021-11-14 | 4 | 0.000829 |
| 2021-11-21 | 4 | 0.000829 |
| 2022-04-18 | 4 | 0.000829 |
| 2020-09-28 | 4 | 0.000829 |
| 2022-05-22 | 4 | 0.000829 |
| 2020-11-15 | 4 | 0.000829 |
| 2022-10-24 | 4 | 0.000829 |
| 2022-04-10 | 4 | 0.000829 |
| 2022-08-20 | 4 | 0.000829 |
| 2021-07-11 | 4 | 0.000829 |
| 2021-08-30 | 4 | 0.000829 |
| 2021-10-24 | 4 | 0.000829 |
| 2021-11-02 | 4 | 0.000829 |
| 2021-05-17 | 4 | 0.000829 |
| 2021-09-26 | 4 | 0.000829 |
| 2021-10-11 | 4 | 0.000829 |
| 2021-03-21 | 4 | 0.000829 |
| 2023-01-16 | 4 | 0.000829 |
| 2020-11-07 | 4 | 0.000829 |
| 2021-06-28 | 4 | 0.000829 |
| 2023-03-12 | 4 | 0.000829 |
| 2021-07-25 | 4 | 0.000829 |
| 2023-07-02 | 4 | 0.000829 |
| 2023-05-28 | 4 | 0.000829 |
| 2022-06-12 | 4 | 0.000829 |
| 2022-07-25 | 4 | 0.000829 |
| 2022-06-26 | 4 | 0.000829 |
| 2023-05-15 | 4 | 0.000829 |
| 2022-07-24 | 3 | 0.000622 |
| 2021-05-07 | 3 | 0.000622 |
| 2023-06-26 | 3 | 0.000622 |
| 2020-10-19 | 3 | 0.000622 |
| 2021-07-05 | 3 | 0.000622 |
| 2021-06-13 | 3 | 0.000622 |
| 2022-04-25 | 3 | 0.000622 |
| 2021-05-23 | 3 | 0.000622 |
| 2022-06-20 | 3 | 0.000622 |
| 2022-08-14 | 3 | 0.000622 |
| 2022-08-29 | 3 | 0.000622 |
| 2021-05-09 | 3 | 0.000622 |
| 2021-03-14 | 3 | 0.000622 |
| 2022-09-11 | 3 | 0.000622 |
| 2022-11-28 | 3 | 0.000622 |
| 2022-10-23 | 3 | 0.000622 |
| 2022-09-19 | 3 | 0.000622 |
| 2022-09-12 | 3 | 0.000622 |
| 2022-09-26 | 3 | 0.000622 |
| 2022-09-25 | 3 | 0.000622 |
| 2022-07-31 | 3 | 0.000622 |
| 2022-10-17 | 3 | 0.000622 |
| 2021-07-12 | 3 | 0.000622 |
| 2023-03-20 | 3 | 0.000622 |
| 2021-09-06 | 3 | 0.000622 |
| 2023-03-27 | 3 | 0.000622 |
| 2021-11-22 | 3 | 0.000622 |
| 2023-05-01 | 3 | 0.000622 |
| 2023-04-24 | 3 | 0.000622 |
| 2020-10-02 | 3 | 0.000622 |
| 2021-10-25 | 3 | 0.000622 |
| 2021-09-05 | 3 | 0.000622 |
| 2021-08-29 | 3 | 0.000622 |
| 2023-07-03 | 3 | 0.000622 |
| 2021-07-19 | 3 | 0.000622 |
| 2022-08-01 | 2 | 0.000415 |
| 2022-08-15 | 2 | 0.000415 |
| 2023-03-19 | 2 | 0.000415 |
| 2022-08-21 | 2 | 0.000415 |
| 2023-04-10 | 2 | 0.000415 |
| 2022-10-16 | 2 | 0.000415 |
| 2023-05-29 | 2 | 0.000415 |
| 2022-08-22 | 2 | 0.000415 |
| 2023-06-18 | 2 | 0.000415 |
| 2020-10-11 | 2 | 0.000415 |
| 2021-07-18 | 2 | 0.000415 |
| 2021-12-06 | 2 | 0.000415 |
| 2021-09-20 | 2 | 0.000415 |
| 2022-02-14 | 2 | 0.000415 |
| 2021-10-18 | 2 | 0.000415 |
| 2023-01-22 | 2 | 0.000415 |
| 2021-11-28 | 2 | 0.000415 |
| 2021-09-27 | 2 | 0.000415 |
| 2021-08-05 | 2 | 0.000415 |
| 2022-05-29 | 2 | 0.000415 |
| 2022-01-04 | 1 | 0.000207 |
| 2023-07-04 | 1 | 0.000207 |
| 2023-05-14 | 1 | 0.000207 |
| 2022-11-14 | 1 | 0.000207 |
| 2022-06-27 | 1 | 0.000207 |
| 2021-07-06 | 1 | 0.000207 |
| 2023-05-21 | 1 | 0.000207 |
| 2021-05-14 | 1 | 0.000207 |
| 2021-07-26 | 1 | 0.000207 |
| 2022-10-13 | 1 | 0.000207 |
| 2023-06-11 | 1 | 0.000207 |
| 2022-04-17 | 1 | 0.000207 |
| 2022-08-28 | 1 | 0.000207 |
| 2021-08-03 | 1 | 0.000207 |
| 2022-09-18 | 1 | 0.000207 |
| 2022-05-02 | 1 | 0.000207 |
| 2021-09-19 | 1 | 0.000207 |
| 2022-05-30 | 1 | 0.000207 |
| 2022-04-24 | 1 | 0.000207 |
| 2023-04-16 | 1 | 0.000207 |
| 2022-11-02 | 1 | 0.000207 |
| 2021-11-08 | 1 | 0.000207 |
| 2021-05-30 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_datefirstdonation__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datefirstdonation__c es 302093. Lo que supone un 62.64578287268987% El nº de vacios para la variable msf_datefirstdonation__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2017-12-01 | 3553 | 1.972453 |
| 2010-02-01 | 3135 | 1.740400 |
| 2020-07-01 | 2483 | 1.378441 |
| 2014-11-01 | 2270 | 1.260194 |
| 2003-08-01 | 1864 | 1.034802 |
| ... | ... | ... |
| 2012-08-29 | 1 | 0.000555 |
| 2010-07-28 | 1 | 0.000555 |
| 2007-07-24 | 1 | 0.000555 |
| 2012-04-02 | 1 | 0.000555 |
| 2012-03-05 | 1 | 0.000555 |
8337 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_datefirstrecurringdonorquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datefirstrecurringdonorquota__c es 769. Lo que supone un 0.15946945817711272% El nº de vacios para la variable msf_datefirstrecurringdonorquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2011-01-03 | 5579 | 1.158779 |
| 2021-01-05 | 5144 | 1.068428 |
| 2015-01-02 | 4977 | 1.033741 |
| 2009-01-02 | 4963 | 1.030834 |
| 2014-11-03 | 4670 | 0.969976 |
| 2014-12-02 | 4658 | 0.967484 |
| 2006-01-05 | 4547 | 0.944429 |
| 2012-01-02 | 4534 | 0.941729 |
| 2016-07-01 | 4480 | 0.930513 |
| 2005-02-04 | 4171 | 0.866332 |
| 2015-10-01 | 4139 | 0.859686 |
| 2004-02-01 | 3943 | 0.818976 |
| 2016-01-04 | 3748 | 0.778474 |
| 2017-04-03 | 3726 | 0.773904 |
| 2005-01-04 | 3565 | 0.740464 |
| 2016-08-01 | 3524 | 0.731948 |
| 2015-11-03 | 3512 | 0.729456 |
| 2013-01-02 | 3440 | 0.714501 |
| 2010-01-04 | 3417 | 0.709724 |
| 2015-12-02 | 3378 | 0.701623 |
| 2016-04-01 | 3346 | 0.694977 |
| 2003-03-01 | 3318 | 0.689161 |
| 2015-02-02 | 3264 | 0.677945 |
| 2014-05-05 | 3207 | 0.666106 |
| 2016-10-03 | 3193 | 0.663198 |
| 2017-06-01 | 3186 | 0.661744 |
| 2015-03-02 | 3122 | 0.648451 |
| 2017-01-02 | 3114 | 0.646789 |
| 2016-12-01 | 3108 | 0.645543 |
| 2017-12-04 | 3031 | 0.629550 |
| 2004-01-01 | 3016 | 0.626434 |
| 2007-01-04 | 3000 | 0.623111 |
| 2017-07-03 | 2993 | 0.621657 |
| 2016-05-02 | 2988 | 0.620619 |
| 2016-11-02 | 2968 | 0.616465 |
| 2016-06-01 | 2923 | 0.607118 |
| 2017-03-02 | 2906 | 0.603587 |
| 2023-07-04 | 2880 | 0.598187 |
| 2016-03-01 | 2873 | 0.596733 |
| 2010-02-01 | 2859 | 0.593825 |
| 2017-02-02 | 2856 | 0.593202 |
| 2015-04-01 | 2846 | 0.591125 |
| 2018-01-03 | 2842 | 0.590294 |
| 2016-02-01 | 2824 | 0.586555 |
| 2015-05-04 | 2823 | 0.586348 |
| 2003-01-01 | 2800 | 0.581570 |
| 2009-02-03 | 2791 | 0.579701 |
| 2014-01-02 | 2767 | 0.574716 |
| 2017-08-01 | 2695 | 0.559762 |
| 2023-06-02 | 2659 | 0.552284 |
| 2018-02-01 | 2646 | 0.549584 |
| 2017-05-02 | 2623 | 0.544807 |
| 2015-06-02 | 2618 | 0.543768 |
| 2022-04-02 | 2567 | 0.533175 |
| 2006-02-03 | 2541 | 0.527775 |
| 2018-03-01 | 2511 | 0.521544 |
| 2014-06-05 | 2444 | 0.507628 |
| 2015-08-03 | 2444 | 0.507628 |
| 2018-06-01 | 2417 | 0.502020 |
| 2023-03-02 | 2389 | 0.496204 |
| 2013-02-01 | 2369 | 0.492050 |
| 2023-04-04 | 2342 | 0.486442 |
| 2012-02-01 | 2340 | 0.486027 |
| 2010-12-02 | 2302 | 0.478134 |
| 2017-09-01 | 2297 | 0.477095 |
| 2014-08-01 | 2284 | 0.474395 |
| 2014-04-02 | 2279 | 0.473357 |
| 2018-07-02 | 2271 | 0.471695 |
| 2009-12-02 | 2267 | 0.470864 |
| 2015-07-01 | 2260 | 0.469410 |
| 2000-02-01 | 2253 | 0.467957 |
| 2017-11-02 | 2247 | 0.466710 |
| 2018-12-03 | 2247 | 0.466710 |
| 2014-10-02 | 2220 | 0.461102 |
| 2019-01-02 | 2210 | 0.459025 |
| 2020-02-03 | 2208 | 0.458610 |
| 2022-12-02 | 2197 | 0.456325 |
| 2014-02-03 | 2192 | 0.455287 |
| 2018-08-01 | 2185 | 0.453833 |
| 2017-10-02 | 2145 | 0.445525 |
| 2020-01-02 | 2123 | 0.440955 |
| 2018-11-02 | 2108 | 0.437839 |
| 2011-12-01 | 2093 | 0.434724 |
| 2007-02-05 | 2083 | 0.432647 |
| 2022-07-05 | 2079 | 0.431816 |
| 2023-02-02 | 2076 | 0.431193 |
| 2019-12-02 | 2070 | 0.429947 |
| 2011-02-01 | 2060 | 0.427870 |
| 2016-09-01 | 2034 | 0.422469 |
| 2018-04-03 | 2033 | 0.422262 |
| 2005-03-04 | 1978 | 0.410838 |
| 2019-11-04 | 1962 | 0.407515 |
| 2022-11-03 | 1946 | 0.404191 |
| 2014-07-02 | 1941 | 0.403153 |
| 2020-03-02 | 1939 | 0.402738 |
| 2019-05-02 | 1936 | 0.402114 |
| 2019-06-03 | 1913 | 0.397337 |
| 2019-02-01 | 1909 | 0.396506 |
| 2013-05-02 | 1869 | 0.388198 |
| 2019-07-01 | 1859 | 0.386121 |
| 2018-05-03 | 1855 | 0.385290 |
| 2013-12-02 | 1842 | 0.382590 |
| 2021-03-02 | 1840 | 0.382175 |
| 2000-01-01 | 1833 | 0.380721 |
| 2014-03-03 | 1829 | 0.379890 |
| 2019-08-01 | 1828 | 0.379682 |
| 1994-10-01 | 1820 | 0.378021 |
| 2001-03-01 | 1815 | 0.376982 |
| 2008-12-01 | 1800 | 0.373867 |
| 2019-04-01 | 1799 | 0.373659 |
| 2023-01-03 | 1787 | 0.371167 |
| 2023-05-03 | 1783 | 0.370336 |
| 2022-06-02 | 1775 | 0.368674 |
| 2022-10-04 | 1774 | 0.368466 |
| 2021-04-02 | 1774 | 0.368466 |
| 2013-11-04 | 1767 | 0.367012 |
| 2021-07-02 | 1746 | 0.362651 |
| 2018-10-02 | 1728 | 0.358912 |
| 2020-05-03 | 1727 | 0.358704 |
| 2022-08-02 | 1716 | 0.356420 |
| 2019-10-02 | 1708 | 0.354758 |
| 2013-08-02 | 1707 | 0.354550 |
| 2014-09-03 | 1701 | 0.353304 |
| 2019-03-01 | 1699 | 0.352889 |
| 2013-06-03 | 1687 | 0.350396 |
| 1995-02-01 | 1677 | 0.348319 |
| 2021-06-02 | 1664 | 0.345619 |
| 2008-02-04 | 1642 | 0.341050 |
| 2022-01-04 | 1627 | 0.337934 |
| 2013-04-02 | 1596 | 0.331495 |
| 2004-03-01 | 1581 | 0.328380 |
| 2012-12-03 | 1573 | 0.326718 |
| 2021-12-02 | 1572 | 0.326510 |
| 2015-09-01 | 1543 | 0.320487 |
| 2021-05-04 | 1542 | 0.320279 |
| 2021-02-02 | 1540 | 0.319864 |
| 2022-05-03 | 1534 | 0.318618 |
| 1998-03-01 | 1533 | 0.318410 |
| 2021-10-02 | 1526 | 0.316956 |
| 2011-04-01 | 1525 | 0.316748 |
| 2011-08-02 | 1517 | 0.315087 |
| 2001-02-01 | 1515 | 0.314671 |
| 2022-03-02 | 1502 | 0.311971 |
| 2020-04-02 | 1499 | 0.311348 |
| 2022-02-02 | 1497 | 0.310932 |
| 2018-09-03 | 1495 | 0.310517 |
| 2013-03-01 | 1493 | 0.310102 |
| 2013-07-01 | 1477 | 0.306778 |
| 2012-11-02 | 1475 | 0.306363 |
| 2011-03-01 | 1472 | 0.305740 |
| 2021-11-03 | 1463 | 0.303871 |
| 2008-01-03 | 1443 | 0.299716 |
| 2006-12-02 | 1393 | 0.289331 |
| 1994-02-01 | 1360 | 0.282477 |
| 1999-01-01 | 1331 | 0.276454 |
| 2021-08-03 | 1316 | 0.273338 |
| 2010-03-01 | 1313 | 0.272715 |
| 2013-10-02 | 1306 | 0.271261 |
| 2002-01-01 | 1299 | 0.269807 |
| 2012-08-01 | 1293 | 0.268561 |
| 2009-03-03 | 1292 | 0.268353 |
| 2010-08-02 | 1283 | 0.266484 |
| 2011-11-02 | 1259 | 0.261499 |
| 2013-09-02 | 1240 | 0.257553 |
| 2012-03-01 | 1235 | 0.256514 |
| 2007-12-02 | 1226 | 0.254645 |
| 2012-04-02 | 1226 | 0.254645 |
| 2012-06-04 | 1225 | 0.254437 |
| 2011-09-02 | 1205 | 0.250283 |
| 2020-06-02 | 1188 | 0.246752 |
| 2020-08-03 | 1166 | 0.242183 |
| 2012-07-02 | 1164 | 0.241767 |
| 1999-02-01 | 1144 | 0.237613 |
| 2019-09-02 | 1107 | 0.229928 |
| 2005-12-03 | 1104 | 0.229305 |
| 2011-05-02 | 1062 | 0.220581 |
| 2011-07-01 | 1062 | 0.220581 |
| 1996-02-01 | 1050 | 0.218089 |
| 2010-04-01 | 1033 | 0.214558 |
| 2011-10-04 | 1031 | 0.214143 |
| 2001-01-01 | 1022 | 0.212273 |
| 2012-05-03 | 995 | 0.206665 |
| 2008-08-08 | 988 | 0.205211 |
| 2010-07-01 | 985 | 0.204588 |
| 2006-11-03 | 959 | 0.199188 |
| 2020-07-01 | 959 | 0.199188 |
| 2012-10-01 | 957 | 0.198772 |
| 2011-06-01 | 956 | 0.198565 |
| 1994-07-01 | 931 | 0.193372 |
| 2006-03-03 | 921 | 0.191295 |
| 2007-04-02 | 920 | 0.191087 |
| 2009-07-02 | 912 | 0.189426 |
| 1997-02-01 | 905 | 0.187972 |
| 2009-04-02 | 895 | 0.185895 |
| 2007-03-02 | 889 | 0.184649 |
| 2022-09-02 | 881 | 0.182987 |
| 2008-04-04 | 878 | 0.182364 |
| 2002-12-01 | 877 | 0.182156 |
| 2008-03-03 | 864 | 0.179456 |
| 2010-06-02 | 859 | 0.178418 |
| 1998-02-01 | 815 | 0.169279 |
| 2021-09-02 | 814 | 0.169071 |
| 2007-05-04 | 795 | 0.165124 |
| 2010-10-04 | 792 | 0.164501 |
| 2005-11-03 | 774 | 0.160763 |
| 2008-06-02 | 773 | 0.160555 |
| 2006-04-03 | 772 | 0.160347 |
| 1995-04-01 | 763 | 0.158478 |
| 2007-07-04 | 757 | 0.157232 |
| 2010-05-03 | 749 | 0.155570 |
| 2008-07-04 | 746 | 0.154947 |
| 2005-08-02 | 741 | 0.153908 |
| 2004-12-05 | 735 | 0.152662 |
| 2006-06-02 | 735 | 0.152662 |
| 2009-06-04 | 713 | 0.148093 |
| 2009-05-04 | 706 | 0.146639 |
| 2009-08-03 | 705 | 0.146431 |
| 2006-07-03 | 698 | 0.144977 |
| 1994-01-01 | 691 | 0.143523 |
| 2007-10-04 | 684 | 0.142069 |
| 2005-07-04 | 679 | 0.141031 |
| 2003-12-01 | 675 | 0.140200 |
| 2000-03-01 | 674 | 0.139992 |
| 2009-10-02 | 673 | 0.139785 |
| 2007-09-03 | 672 | 0.139577 |
| 2010-11-02 | 672 | 0.139577 |
| 2007-08-02 | 658 | 0.136669 |
| 2012-09-03 | 657 | 0.136461 |
| 1992-11-01 | 647 | 0.134384 |
| 2005-06-03 | 637 | 0.132307 |
| 2020-09-01 | 625 | 0.129815 |
| 2009-11-02 | 621 | 0.128984 |
| 1995-03-01 | 615 | 0.127738 |
| 2007-06-05 | 597 | 0.123999 |
| 2008-05-02 | 596 | 0.123791 |
| 2009-09-02 | 585 | 0.121507 |
| 2008-09-01 | 582 | 0.120884 |
| 1994-09-01 | 581 | 0.120676 |
| 2010-09-02 | 570 | 0.118391 |
| 1998-01-01 | 556 | 0.115483 |
| 2005-09-02 | 542 | 0.112575 |
| 2005-04-04 | 531 | 0.110291 |
| 2008-10-02 | 522 | 0.108421 |
| 2008-11-03 | 513 | 0.106552 |
| 1999-06-01 | 511 | 0.106137 |
| 2007-11-02 | 505 | 0.104890 |
| 2002-04-01 | 494 | 0.102606 |
| 2005-05-04 | 487 | 0.101152 |
| 1997-01-01 | 480 | 0.099698 |
| 1994-03-01 | 480 | 0.099698 |
| 1999-03-01 | 475 | 0.098659 |
| 2003-06-01 | 458 | 0.095128 |
| 2004-04-01 | 432 | 0.089728 |
| 2005-10-03 | 425 | 0.088274 |
| 2002-02-01 | 425 | 0.088274 |
| 1995-07-01 | 417 | 0.086612 |
| 2003-04-01 | 416 | 0.086405 |
| 2006-08-02 | 408 | 0.084743 |
| 1995-01-01 | 394 | 0.081835 |
| 2002-05-01 | 391 | 0.081212 |
| 2003-08-01 | 383 | 0.079551 |
| 2000-05-01 | 382 | 0.079343 |
| 2001-04-01 | 379 | 0.078720 |
| 2006-05-04 | 372 | 0.077266 |
| 1994-04-01 | 361 | 0.074981 |
| 1999-07-01 | 353 | 0.073319 |
| 2006-09-04 | 339 | 0.070412 |
| 2004-11-04 | 337 | 0.069996 |
| 2006-10-02 | 337 | 0.069996 |
| 2001-08-01 | 332 | 0.068958 |
| 2003-11-01 | 329 | 0.068335 |
| 2004-06-01 | 325 | 0.067504 |
| 1995-10-01 | 325 | 0.067504 |
| 1992-12-01 | 323 | 0.067088 |
| 2000-04-01 | 318 | 0.066050 |
| 2004-05-01 | 317 | 0.065842 |
| 1999-12-01 | 302 | 0.062727 |
| 1996-04-01 | 295 | 0.061273 |
| 1998-04-01 | 293 | 0.060857 |
| 2003-05-01 | 289 | 0.060026 |
| 2001-12-01 | 289 | 0.060026 |
| 1996-12-01 | 273 | 0.056703 |
| 1998-12-01 | 273 | 0.056703 |
| 2000-01-13 | 271 | 0.056288 |
| 1999-05-01 | 269 | 0.055872 |
| 2001-07-01 | 259 | 0.053795 |
| 2004-08-01 | 258 | 0.053588 |
| 1996-03-01 | 257 | 0.053380 |
| 2002-08-01 | 247 | 0.051303 |
| 2002-11-01 | 245 | 0.050887 |
| 1996-01-01 | 243 | 0.050472 |
| 1994-06-01 | 242 | 0.050264 |
| 1996-06-01 | 240 | 0.049849 |
| 1998-11-01 | 239 | 0.049641 |
| 1998-09-01 | 232 | 0.048187 |
| 2004-10-06 | 232 | 0.048187 |
| 1995-06-01 | 230 | 0.047772 |
| 2002-03-01 | 224 | 0.046526 |
| 1998-05-01 | 220 | 0.045695 |
| 1992-06-01 | 217 | 0.045072 |
| 1993-01-01 | 215 | 0.044656 |
| 1997-03-01 | 212 | 0.044033 |
| 2004-07-01 | 212 | 0.044033 |
| 1993-11-01 | 211 | 0.043825 |
| 2000-06-01 | 206 | 0.042787 |
| 2003-10-01 | 204 | 0.042372 |
| 1993-03-01 | 194 | 0.040295 |
| 1998-06-01 | 193 | 0.040087 |
| 1993-07-01 | 189 | 0.039256 |
| 2003-09-01 | 186 | 0.038633 |
| 1994-08-01 | 186 | 0.038633 |
| 1996-07-01 | 177 | 0.036764 |
| 2020-12-02 | 175 | 0.036348 |
| 1999-04-01 | 175 | 0.036348 |
| 2002-09-01 | 172 | 0.035725 |
| 2003-07-01 | 164 | 0.034063 |
| 1994-05-01 | 163 | 0.033856 |
| 1994-12-01 | 163 | 0.033856 |
| 2000-07-01 | 159 | 0.033025 |
| 1997-11-01 | 157 | 0.032609 |
| 2002-10-01 | 156 | 0.032402 |
| 2004-09-03 | 154 | 0.031986 |
| 1995-05-01 | 153 | 0.031779 |
| 1993-02-01 | 151 | 0.031363 |
| 2003-02-01 | 149 | 0.030948 |
| 2001-11-01 | 145 | 0.030117 |
| 1999-08-01 | 135 | 0.028040 |
| 1993-12-01 | 135 | 0.028040 |
| 1995-12-01 | 123 | 0.025548 |
| 1995-11-01 | 123 | 0.025548 |
| 1995-09-01 | 115 | 0.023886 |
| 1994-11-01 | 115 | 0.023886 |
| 1994-01-11 | 115 | 0.023886 |
| 2001-05-01 | 112 | 0.023263 |
| 1996-05-01 | 111 | 0.023055 |
| 1997-06-01 | 111 | 0.023055 |
| 1998-07-01 | 110 | 0.022847 |
| 1997-12-01 | 105 | 0.021809 |
| 1998-08-01 | 104 | 0.021601 |
| 2020-10-02 | 101 | 0.020978 |
| 1996-08-01 | 94 | 0.019524 |
| 2001-09-01 | 93 | 0.019316 |
| 2000-04-05 | 92 | 0.019109 |
| 1997-05-01 | 91 | 0.018901 |
| 1992-07-01 | 90 | 0.018693 |
| 2001-10-01 | 89 | 0.018486 |
| 1993-10-01 | 87 | 0.018070 |
| 2000-08-01 | 84 | 0.017447 |
| 2000-12-01 | 84 | 0.017447 |
| 1997-07-01 | 83 | 0.017239 |
| 1996-09-01 | 82 | 0.017032 |
| 1999-11-01 | 81 | 0.016824 |
| 1993-06-01 | 79 | 0.016409 |
| 1999-09-01 | 77 | 0.015993 |
| 1997-04-01 | 75 | 0.015578 |
| 1996-10-01 | 73 | 0.015162 |
| 1993-05-01 | 73 | 0.015162 |
| 2001-06-01 | 70 | 0.014539 |
| 1992-08-01 | 68 | 0.014124 |
| 1999-10-01 | 68 | 0.014124 |
| 2000-11-01 | 67 | 0.013916 |
| 1996-11-01 | 65 | 0.013501 |
| 2002-06-17 | 64 | 0.013293 |
| 2000-03-09 | 63 | 0.013085 |
| 2000-10-01 | 63 | 0.013085 |
| 1995-08-01 | 63 | 0.013085 |
| 2020-11-04 | 62 | 0.012878 |
| 2000-09-01 | 60 | 0.012462 |
| 1997-08-01 | 60 | 0.012462 |
| 1993-04-01 | 57 | 0.011839 |
| 1997-09-01 | 56 | 0.011631 |
| 2002-06-13 | 53 | 0.011008 |
| 1992-10-01 | 51 | 0.010593 |
| 2002-06-07 | 50 | 0.010385 |
| 1998-10-01 | 49 | 0.010177 |
| 1992-09-01 | 47 | 0.009762 |
| 2002-06-12 | 46 | 0.009554 |
| 1993-08-01 | 43 | 0.008931 |
| 1997-10-01 | 41 | 0.008516 |
| 2002-06-11 | 39 | 0.008100 |
| 1993-09-01 | 39 | 0.008100 |
| 1991-01-20 | 37 | 0.007685 |
| 2021-02-05 | 36 | 0.007477 |
| 2002-06-19 | 35 | 0.007270 |
| 2015-02-01 | 34 | 0.007062 |
| 2002-06-14 | 33 | 0.006854 |
| 1991-02-20 | 33 | 0.006854 |
| 2015-03-01 | 32 | 0.006647 |
| 2002-06-06 | 32 | 0.006647 |
| 2002-07-05 | 29 | 0.006023 |
| 2017-01-01 | 27 | 0.005608 |
| 1992-01-02 | 26 | 0.005400 |
| 2002-06-10 | 26 | 0.005400 |
| 2015-01-01 | 26 | 0.005400 |
| 2015-06-01 | 25 | 0.005193 |
| 2015-11-01 | 25 | 0.005193 |
| 2014-12-01 | 24 | 0.004985 |
| 2017-07-01 | 24 | 0.004985 |
| 1991-08-01 | 23 | 0.004777 |
| 2016-01-01 | 22 | 0.004569 |
| 1991-12-01 | 21 | 0.004362 |
| 2017-10-01 | 20 | 0.004154 |
| 2016-11-01 | 20 | 0.004154 |
| 2014-07-01 | 19 | 0.003946 |
| 2015-12-01 | 19 | 0.003946 |
| 2017-04-01 | 19 | 0.003946 |
| 2015-05-01 | 19 | 0.003946 |
| 2016-05-01 | 18 | 0.003739 |
| 2002-07-04 | 18 | 0.003739 |
| 1991-11-15 | 18 | 0.003739 |
| 2014-01-01 | 18 | 0.003739 |
| 2013-11-01 | 17 | 0.003531 |
| 2017-11-01 | 17 | 0.003531 |
| 2017-03-01 | 17 | 0.003531 |
| 2014-05-01 | 17 | 0.003531 |
| 2017-02-01 | 16 | 0.003323 |
| 2017-05-01 | 16 | 0.003323 |
| 2014-09-01 | 15 | 0.003116 |
| 1991-11-01 | 15 | 0.003116 |
| 2020-02-01 | 15 | 0.003116 |
| 2014-03-01 | 15 | 0.003116 |
| 2002-06-28 | 15 | 0.003116 |
| 2015-08-01 | 15 | 0.003116 |
| 1991-07-01 | 15 | 0.003116 |
| 2013-09-01 | 15 | 0.003116 |
| 1991-11-06 | 15 | 0.003116 |
| 2019-01-01 | 14 | 0.002908 |
| 2013-12-01 | 14 | 0.002908 |
| 1992-03-02 | 14 | 0.002908 |
| 2012-01-01 | 14 | 0.002908 |
| 2002-06-21 | 14 | 0.002908 |
| 2014-11-01 | 13 | 0.002700 |
| 2002-06-18 | 13 | 0.002700 |
| 2012-04-01 | 13 | 0.002700 |
| 1992-06-02 | 13 | 0.002700 |
| 1991-10-01 | 13 | 0.002700 |
| 2012-07-01 | 13 | 0.002700 |
| 2020-01-01 | 13 | 0.002700 |
| 2012-06-01 | 12 | 0.002492 |
| 1992-02-02 | 12 | 0.002492 |
| 1991-01-21 | 12 | 0.002492 |
| 2018-01-01 | 12 | 0.002492 |
| 2018-09-01 | 12 | 0.002492 |
| 2013-08-01 | 12 | 0.002492 |
| 2012-09-01 | 11 | 0.002285 |
| 1991-06-03 | 11 | 0.002285 |
| 2018-07-01 | 11 | 0.002285 |
| 2009-02-01 | 11 | 0.002285 |
| 2020-04-01 | 11 | 0.002285 |
| 2018-05-01 | 11 | 0.002285 |
| 1991-03-25 | 11 | 0.002285 |
| 2014-02-01 | 11 | 0.002285 |
| 2014-10-01 | 11 | 0.002285 |
| 2018-12-01 | 11 | 0.002285 |
| 2009-03-01 | 11 | 0.002285 |
| 2010-09-01 | 10 | 0.002077 |
| 1991-11-11 | 10 | 0.002077 |
| 2010-12-01 | 10 | 0.002077 |
| 2011-05-01 | 10 | 0.002077 |
| 2017-12-01 | 10 | 0.002077 |
| 1994-10-06 | 10 | 0.002077 |
| 2013-04-01 | 10 | 0.002077 |
| 2018-04-01 | 10 | 0.002077 |
| 2019-06-01 | 10 | 0.002077 |
| 2016-10-01 | 10 | 0.002077 |
| 2020-03-01 | 10 | 0.002077 |
| 2014-04-01 | 10 | 0.002077 |
| 2007-03-01 | 10 | 0.002077 |
| 2018-11-01 | 9 | 0.001869 |
| 2019-11-01 | 9 | 0.001869 |
| 2019-12-01 | 9 | 0.001869 |
| 2008-02-01 | 9 | 0.001869 |
| 2010-01-01 | 9 | 0.001869 |
| 2008-03-01 | 9 | 0.001869 |
| 2013-05-01 | 9 | 0.001869 |
| 2014-06-01 | 8 | 0.001662 |
| 1992-01-14 | 8 | 0.001662 |
| 2019-05-01 | 8 | 0.001662 |
| 2020-05-01 | 8 | 0.001662 |
| 2012-11-01 | 8 | 0.001662 |
| 2013-06-01 | 8 | 0.001662 |
| 1994-03-28 | 8 | 0.001662 |
| 2002-07-03 | 8 | 0.001662 |
| 2013-10-01 | 8 | 0.001662 |
| 2019-09-01 | 7 | 0.001454 |
| 2009-08-01 | 7 | 0.001454 |
| 1991-09-01 | 7 | 0.001454 |
| 2018-10-01 | 7 | 0.001454 |
| 2011-01-01 | 7 | 0.001454 |
| 2010-08-01 | 7 | 0.001454 |
| 2011-09-01 | 7 | 0.001454 |
| 2012-05-01 | 7 | 0.001454 |
| 2019-10-01 | 7 | 0.001454 |
| 2011-11-01 | 7 | 0.001454 |
| 2006-05-01 | 6 | 0.001246 |
| 2009-09-01 | 6 | 0.001246 |
| 2008-06-01 | 6 | 0.001246 |
| 2011-08-01 | 6 | 0.001246 |
| 2007-02-01 | 6 | 0.001246 |
| 2006-12-01 | 6 | 0.001246 |
| 2008-10-01 | 5 | 0.001039 |
| 2013-01-01 | 5 | 0.001039 |
| 2008-04-01 | 5 | 0.001039 |
| 2008-08-01 | 5 | 0.001039 |
| 2007-08-01 | 5 | 0.001039 |
| 2009-12-01 | 5 | 0.001039 |
| 1995-10-02 | 5 | 0.001039 |
| 2012-12-01 | 5 | 0.001039 |
| 2007-01-01 | 5 | 0.001039 |
| 1992-02-01 | 5 | 0.001039 |
| 2008-05-01 | 5 | 0.001039 |
| 1993-11-08 | 5 | 0.001039 |
| 1991-05-16 | 4 | 0.000831 |
| 2010-05-01 | 4 | 0.000831 |
| 2009-06-01 | 4 | 0.000831 |
| 2020-06-01 | 4 | 0.000831 |
| 2007-04-01 | 4 | 0.000831 |
| 2007-10-01 | 4 | 0.000831 |
| 1996-01-02 | 4 | 0.000831 |
| 1992-12-14 | 4 | 0.000831 |
| 2011-10-01 | 4 | 0.000831 |
| 1993-12-07 | 4 | 0.000831 |
| 2010-11-01 | 4 | 0.000831 |
| 1995-01-02 | 4 | 0.000831 |
| 2009-11-01 | 4 | 0.000831 |
| 1993-10-04 | 3 | 0.000623 |
| 1994-03-04 | 3 | 0.000623 |
| 1992-04-01 | 3 | 0.000623 |
| 2007-11-01 | 3 | 0.000623 |
| 2006-10-01 | 3 | 0.000623 |
| 2010-10-01 | 3 | 0.000623 |
| 1993-04-05 | 3 | 0.000623 |
| 1992-09-25 | 3 | 0.000623 |
| 2008-11-01 | 3 | 0.000623 |
| 2006-04-01 | 3 | 0.000623 |
| 2002-06-26 | 3 | 0.000623 |
| 2002-06-05 | 3 | 0.000623 |
| 1994-07-04 | 3 | 0.000623 |
| 1992-01-16 | 3 | 0.000623 |
| 2005-12-01 | 3 | 0.000623 |
| 1995-05-02 | 2 | 0.000415 |
| 1990-12-10 | 2 | 0.000415 |
| 2009-10-01 | 2 | 0.000415 |
| 2010-06-01 | 2 | 0.000415 |
| 2006-06-01 | 2 | 0.000415 |
| 1994-01-07 | 2 | 0.000415 |
| 2008-07-01 | 2 | 0.000415 |
| 1996-09-02 | 2 | 0.000415 |
| 2002-07-01 | 2 | 0.000415 |
| 2008-01-01 | 2 | 0.000415 |
| 2002-06-25 | 2 | 0.000415 |
| 2006-07-01 | 2 | 0.000415 |
| 2007-05-01 | 2 | 0.000415 |
| 1993-09-16 | 2 | 0.000415 |
| 2002-07-02 | 2 | 0.000415 |
| 2006-08-01 | 2 | 0.000415 |
| 1995-09-07 | 2 | 0.000415 |
| 1993-02-09 | 2 | 0.000415 |
| 2007-07-01 | 2 | 0.000415 |
| 2005-09-01 | 2 | 0.000415 |
| 2009-01-01 | 2 | 0.000415 |
| 1993-03-11 | 2 | 0.000415 |
| 2007-12-01 | 2 | 0.000415 |
| 2007-06-01 | 2 | 0.000415 |
| 2005-10-01 | 2 | 0.000415 |
| 1993-01-07 | 2 | 0.000415 |
| 1991-10-25 | 1 | 0.000208 |
| 1992-09-29 | 1 | 0.000208 |
| 1994-02-07 | 1 | 0.000208 |
| 1992-04-02 | 1 | 0.000208 |
| 1995-07-19 | 1 | 0.000208 |
| 1990-02-20 | 1 | 0.000208 |
| 2002-07-27 | 1 | 0.000208 |
| 1990-03-31 | 1 | 0.000208 |
| 1995-10-26 | 1 | 0.000208 |
| 1994-06-06 | 1 | 0.000208 |
| 1992-03-24 | 1 | 0.000208 |
| 1991-01-08 | 1 | 0.000208 |
| 2000-03-30 | 1 | 0.000208 |
| 1996-12-26 | 1 | 0.000208 |
| 1991-01-10 | 1 | 0.000208 |
| 1994-10-10 | 1 | 0.000208 |
| 1991-10-21 | 1 | 0.000208 |
| 2002-07-19 | 1 | 0.000208 |
| 1994-01-10 | 1 | 0.000208 |
| 1991-04-02 | 1 | 0.000208 |
| 1996-12-28 | 1 | 0.000208 |
| 1999-12-28 | 1 | 0.000208 |
| 1992-05-15 | 1 | 0.000208 |
| 1990-04-20 | 1 | 0.000208 |
| 2002-07-17 | 1 | 0.000208 |
| 1992-05-22 | 1 | 0.000208 |
| 2020-08-01 | 1 | 0.000208 |
| 2009-05-01 | 1 | 0.000208 |
| 1990-02-12 | 1 | 0.000208 |
| 1993-04-12 | 1 | 0.000208 |
| 1993-02-04 | 1 | 0.000208 |
| 1993-02-05 | 1 | 0.000208 |
| 1999-03-25 | 1 | 0.000208 |
| 1993-08-05 | 1 | 0.000208 |
| 1994-10-11 | 1 | 0.000208 |
| 1993-04-30 | 1 | 0.000208 |
| 1994-08-19 | 1 | 0.000208 |
| 1995-10-11 | 1 | 0.000208 |
| 1991-12-03 | 1 | 0.000208 |
| 1993-03-16 | 1 | 0.000208 |
| 1996-09-25 | 1 | 0.000208 |
| 2006-09-01 | 1 | 0.000208 |
| 2006-11-01 | 1 | 0.000208 |
| 1996-12-20 | 1 | 0.000208 |
| 2005-11-01 | 1 | 0.000208 |
| 1995-10-21 | 1 | 0.000208 |
| 2002-07-28 | 1 | 0.000208 |
| 1993-02-08 | 1 | 0.000208 |
| 1991-05-08 | 1 | 0.000208 |
| 1992-09-23 | 1 | 0.000208 |
| 1993-02-17 | 1 | 0.000208 |
| 1996-12-21 | 1 | 0.000208 |
| 2009-04-01 | 1 | 0.000208 |
| 1998-08-18 | 1 | 0.000208 |
| 2002-05-28 | 1 | 0.000208 |
| 1994-12-04 | 1 | 0.000208 |
| 1996-12-13 | 1 | 0.000208 |
| 1997-04-02 | 1 | 0.000208 |
| 1990-06-10 | 1 | 0.000208 |
| 1991-04-23 | 1 | 0.000208 |
| 1992-03-01 | 1 | 0.000208 |
| 1991-03-20 | 1 | 0.000208 |
| 1996-09-30 | 1 | 0.000208 |
| 1990-03-01 | 1 | 0.000208 |
| 1998-10-20 | 1 | 0.000208 |
| 1991-06-01 | 1 | 0.000208 |
# Vamos a realizar analisis por cada variable
var = "msf_datelastrecurringdonorquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datelastrecurringdonorquota__c es 769. Lo que supone un 0.15946945817711272% El nº de vacios para la variable msf_datelastrecurringdonorquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2023-07-04 | 391625 | 81.341974 |
| 2023-06-02 | 18460 | 3.834211 |
| 2023-05-03 | 17319 | 3.597221 |
| 2023-02-02 | 9792 | 2.033835 |
| 2023-01-03 | 9783 | 2.031966 |
| 2023-03-02 | 7026 | 1.459326 |
| 2022-12-02 | 6630 | 1.377076 |
| 2023-04-04 | 5758 | 1.195958 |
| 2022-11-03 | 4380 | 0.909742 |
| 2022-08-02 | 4104 | 0.852416 |
| 2022-10-04 | 3661 | 0.760403 |
| 2022-09-02 | 2687 | 0.558100 |
| 2022-07-05 | 10 | 0.002077 |
| 2020-09-01 | 9 | 0.001869 |
| 2018-01-03 | 7 | 0.001454 |
| 2023-07-03 | 6 | 0.001246 |
| 2019-02-01 | 6 | 0.001246 |
| 2018-12-03 | 5 | 0.001039 |
| 2022-06-02 | 5 | 0.001039 |
| 2020-02-03 | 5 | 0.001039 |
| 2020-01-02 | 5 | 0.001039 |
| 2019-11-04 | 4 | 0.000831 |
| 2020-07-01 | 4 | 0.000831 |
| 2018-04-03 | 4 | 0.000831 |
| 2019-08-01 | 4 | 0.000831 |
| 2022-01-04 | 3 | 0.000623 |
| 2018-06-01 | 3 | 0.000623 |
| 2017-07-03 | 3 | 0.000623 |
| 2020-03-02 | 3 | 0.000623 |
| 2020-04-02 | 3 | 0.000623 |
| 2022-02-02 | 3 | 0.000623 |
| 2017-10-02 | 3 | 0.000623 |
| 2020-10-02 | 3 | 0.000623 |
| 2015-09-01 | 3 | 0.000623 |
| 2020-11-04 | 3 | 0.000623 |
| 2018-07-02 | 3 | 0.000623 |
| 2014-01-02 | 3 | 0.000623 |
| 2018-08-01 | 3 | 0.000623 |
| 2015-11-03 | 3 | 0.000623 |
| 2015-05-04 | 2 | 0.000415 |
| 2016-11-02 | 2 | 0.000415 |
| 2017-12-04 | 2 | 0.000415 |
| 2020-06-02 | 2 | 0.000415 |
| 2018-09-03 | 2 | 0.000415 |
| 2023-06-01 | 2 | 0.000415 |
| 2017-01-02 | 2 | 0.000415 |
| 2022-05-03 | 2 | 0.000415 |
| 2020-05-03 | 2 | 0.000415 |
| 2017-02-02 | 2 | 0.000415 |
| 2014-05-05 | 2 | 0.000415 |
| 2019-01-02 | 2 | 0.000415 |
| 2016-03-01 | 2 | 0.000415 |
| 2013-06-03 | 2 | 0.000415 |
| 2014-03-03 | 2 | 0.000415 |
| 2012-12-03 | 2 | 0.000415 |
| 2016-02-01 | 2 | 0.000415 |
| 2016-10-03 | 2 | 0.000415 |
| 2017-08-01 | 2 | 0.000415 |
| 2016-09-01 | 2 | 0.000415 |
| 2017-04-03 | 2 | 0.000415 |
| 2019-04-01 | 2 | 0.000415 |
| 2018-10-02 | 2 | 0.000415 |
| 2017-05-02 | 2 | 0.000415 |
| 2013-10-02 | 2 | 0.000415 |
| 2020-12-02 | 1 | 0.000208 |
| 2006-04-03 | 1 | 0.000208 |
| 2021-10-02 | 1 | 0.000208 |
| 2013-12-02 | 1 | 0.000208 |
| 2019-10-02 | 1 | 0.000208 |
| 2013-08-02 | 1 | 0.000208 |
| 2019-12-02 | 1 | 0.000208 |
| 2014-02-03 | 1 | 0.000208 |
| 2016-08-01 | 1 | 0.000208 |
| 2018-05-01 | 1 | 0.000208 |
| 2012-09-03 | 1 | 0.000208 |
| 2018-11-02 | 1 | 0.000208 |
| 2016-05-02 | 1 | 0.000208 |
| 2010-07-01 | 1 | 0.000208 |
| 2014-06-05 | 1 | 0.000208 |
| 2011-01-03 | 1 | 0.000208 |
| 2014-07-02 | 1 | 0.000208 |
| 2021-02-02 | 1 | 0.000208 |
| 2021-12-02 | 1 | 0.000208 |
| 2015-05-01 | 1 | 0.000208 |
| 2007-08-02 | 1 | 0.000208 |
| 2016-06-01 | 1 | 0.000208 |
| 2009-04-02 | 1 | 0.000208 |
| 2012-05-03 | 1 | 0.000208 |
| 2021-09-02 | 1 | 0.000208 |
| 2019-07-01 | 1 | 0.000208 |
| 2016-04-01 | 1 | 0.000208 |
| 2019-05-02 | 1 | 0.000208 |
| 2011-08-02 | 1 | 0.000208 |
| 2015-12-02 | 1 | 0.000208 |
| 2010-05-03 | 1 | 0.000208 |
| 2018-05-03 | 1 | 0.000208 |
| 2022-12-01 | 1 | 0.000208 |
| 2018-09-01 | 1 | 0.000208 |
| 2017-11-02 | 1 | 0.000208 |
| 2021-01-05 | 1 | 0.000208 |
| 2009-10-02 | 1 | 0.000208 |
| 2019-06-03 | 1 | 0.000208 |
| 2021-11-03 | 1 | 0.000208 |
| 2021-06-02 | 1 | 0.000208 |
| 2017-09-01 | 1 | 0.000208 |
| 2018-03-01 | 1 | 0.000208 |
| 2010-01-04 | 1 | 0.000208 |
| 2009-02-03 | 1 | 0.000208 |
| 2012-03-01 | 1 | 0.000208 |
| 2013-04-02 | 1 | 0.000208 |
| 2016-07-01 | 1 | 0.000208 |
| 2009-09-02 | 1 | 0.000208 |
| 2013-03-01 | 1 | 0.000208 |
| 2010-03-01 | 1 | 0.000208 |
| 2015-03-02 | 1 | 0.000208 |
| 2021-07-02 | 1 | 0.000208 |
| 2021-08-03 | 1 | 0.000208 |
| 2018-02-01 | 1 | 0.000208 |
| 2015-02-02 | 1 | 0.000208 |
| 2010-06-02 | 1 | 0.000208 |
| 2016-12-01 | 1 | 0.000208 |
| 2006-06-02 | 1 | 0.000208 |
| 2010-08-02 | 1 | 0.000208 |
| 2011-03-01 | 1 | 0.000208 |
| 2013-01-02 | 1 | 0.000208 |
| 2015-10-01 | 1 | 0.000208 |
| 2019-03-01 | 1 | 0.000208 |
| 2011-10-04 | 1 | 0.000208 |
# Vamos a realizar analisis por cada variable
var = "msf_datelastdonation__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_datelastdonation__c es 301052. Lo que supone un 62.429908092504725% El nº de vacios para la variable msf_datelastdonation__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2022-12-02 | 6165 | 3.402844 |
| 2023-03-02 | 5664 | 3.126311 |
| 2020-07-01 | 4689 | 2.588148 |
| 2023-07-04 | 3146 | 1.736471 |
| 2021-12-02 | 3056 | 1.686795 |
| ... | ... | ... |
| 1995-01-27 | 1 | 0.000552 |
| 2005-07-07 | 1 | 0.000552 |
| 2010-03-07 | 1 | 0.000552 |
| 2016-08-17 | 1 | 0.000552 |
| 2009-07-16 | 1 | 0.000552 |
6537 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__largest_soft_credit_date__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npsp__largest_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__first_soft_credit_date__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npsp__first_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_entrydatecurrentrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_entrydatecurrentrecurringdonor__c es 1. Lo que supone un 0.0002073725073824613% El nº de vacios para la variable msf_entrydatecurrentrecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2000-02-01 | 1895 | 0.392972 |
| 2000-01-01 | 1558 | 0.323087 |
| 1994-10-01 | 1513 | 0.313755 |
| 1995-02-01 | 1431 | 0.296751 |
| 2004-01-01 | 1350 | 0.279953 |
| ... | ... | ... |
| 2008-04-19 | 1 | 0.000207 |
| 2009-07-11 | 1 | 0.000207 |
| 2002-08-30 | 1 | 0.000207 |
| 1996-09-25 | 1 | 0.000207 |
| 2014-05-10 | 1 | 0.000207 |
7586 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__last_soft_credit_date__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npsp__last_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_firstentrydaterecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstentrydaterecurringdonor__c es 1. Lo que supone un 0.0002073725073824613% El nº de vacios para la variable msf_firstentrydaterecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2000-02-01 | 2282 | 0.473225 |
| 2000-01-01 | 1854 | 0.384469 |
| 1994-10-01 | 1851 | 0.383847 |
| 2004-01-01 | 1834 | 0.380322 |
| 1995-02-01 | 1698 | 0.352119 |
| ... | ... | ... |
| 2008-04-12 | 1 | 0.000207 |
| 1996-11-27 | 1 | 0.000207 |
| 1991-04-02 | 1 | 0.000207 |
| 2002-08-02 | 1 | 0.000207 |
| 2011-06-25 | 1 | 0.000207 |
7662 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__firstclosedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__firstclosedate__c es 825. Lo que supone un 0.17108231859053055% El nº de vacios para la variable npo02__firstclosedate__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2016-12-01 | 29229 | 6.071679 |
| 2015-12-02 | 27769 | 5.768396 |
| 2017-12-04 | 26675 | 5.541142 |
| 2014-12-02 | 24859 | 5.163908 |
| 2013-12-02 | 15927 | 3.308482 |
| ... | ... | ... |
| 2000-03-24 | 1 | 0.000208 |
| 1996-06-17 | 1 | 0.000208 |
| 2015-02-16 | 1 | 0.000208 |
| 2009-01-16 | 1 | 0.000208 |
| 2012-03-05 | 1 | 0.000208 |
6745 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastrecurringdonationdate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lastrecurringdonationdate__c es 420974. Lo que supone un 87.29843392282424% El nº de vacios para la variable msf_lastrecurringdonationdate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2014-03-13 | 842 | 1.374694 |
| 2016-02-04 | 372 | 0.607347 |
| 2020-03-12 | 304 | 0.496327 |
| 2020-09-01 | 248 | 0.404898 |
| 2014-02-07 | 228 | 0.372245 |
| ... | ... | ... |
| 2004-01-07 | 1 | 0.001633 |
| 2010-03-02 | 1 | 0.001633 |
| 2016-06-16 | 1 | 0.001633 |
| 2003-06-27 | 1 | 0.001633 |
| 2019-11-01 | 1 | 0.001633 |
5090 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__lastclosedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__lastclosedate__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npo02__lastclosedate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "gender__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable gender__c es 0. Lo que supone un 0.0% El nº de vacios para la variable gender__c es 8953. Lo que supone un 1.8566060585951758%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Female | 271434 | 56.287949 |
| Male | 199744 | 41.421414 |
| 8953 | 1.856606 | |
| Other | 2091 | 0.433616 |
| H | 1 | 0.000207 |
| M | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_languagepreferer__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| ESP | 416856 | 86.444474 |
| CAT | 56510 | 11.718620 |
| GAL | 5613 | 1.163982 |
| EUS | 3238 | 0.671472 |
| ING | 7 | 0.001452 |
# Vamos a realizar analisis por cada variable
var = "npo02__largestamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__largestamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 482224 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__smallestamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 482224 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__first_soft_credit_amount__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npsp__first_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__lastoppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__lastoppamount__c es 107. Lo que supone un 0.022188858289923355% El nº de vacios para la variable npo02__lastoppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 10.00 | 79772 | 16.546191 |
| 15.00 | 54933 | 11.394122 |
| 20.00 | 42410 | 8.796620 |
| 5.00 | 29168 | 6.049984 |
| 12.00 | 26997 | 5.599678 |
| 30.00 | 25561 | 5.301825 |
| 25.00 | 17083 | 3.543331 |
| 6.00 | 14790 | 3.067720 |
| 50.00 | 13703 | 2.842256 |
| 60.00 | 10554 | 2.189095 |
| 14.00 | 10412 | 2.159642 |
| 8.00 | 9950 | 2.063814 |
| 100.00 | 9378 | 1.945171 |
| 7.00 | 8906 | 1.847269 |
| 3.00 | 8838 | 1.833165 |
| 40.00 | 8616 | 1.787118 |
| 17.00 | 6080 | 1.261105 |
| 35.00 | 5612 | 1.164033 |
| 18.00 | 5262 | 1.091436 |
| 150.00 | 4300 | 0.891900 |
| 16.00 | 4215 | 0.874269 |
| 9.00 | 3910 | 0.811006 |
| 13.00 | 3725 | 0.772634 |
| 22.00 | 3686 | 0.764545 |
| 26.00 | 3419 | 0.709164 |
| 120.00 | 3412 | 0.707712 |
| 11.00 | 3008 | 0.623915 |
| 19.00 | 2920 | 0.605662 |
| 45.00 | 2876 | 0.596536 |
| 200.00 | 2785 | 0.577661 |
| 70.00 | 2472 | 0.512739 |
| 23.00 | 2277 | 0.472292 |
| 75.00 | 2234 | 0.463373 |
| 30.05 | 2230 | 0.462543 |
| 80.00 | 2142 | 0.444290 |
| 6.01 | 1972 | 0.409029 |
| 90.00 | 1951 | 0.404674 |
| 36.00 | 1603 | 0.332492 |
| 60.10 | 1524 | 0.316106 |
| 32.00 | 1502 | 0.311543 |
| 24.00 | 1496 | 0.310298 |
| 4.33 | 1486 | 0.308224 |
| 4.00 | 1379 | 0.286030 |
| 18.03 | 1340 | 0.277941 |
| 300.00 | 1336 | 0.277111 |
| 28.00 | 1203 | 0.249524 |
| 21.00 | 1161 | 0.240813 |
| 65.00 | 1142 | 0.236872 |
| 250.00 | 1107 | 0.229612 |
| 55.00 | 1092 | 0.226501 |
| 27.00 | 1090 | 0.226086 |
| 42.00 | 954 | 0.197877 |
| 33.00 | 905 | 0.187714 |
| 125.00 | 744 | 0.154319 |
| 0.00 | 718 | 0.148927 |
| 130.00 | 702 | 0.145608 |
| 12.02 | 695 | 0.144156 |
| 180.00 | 677 | 0.140422 |
| 110.00 | 641 | 0.132955 |
| 140.00 | 558 | 0.115740 |
| 72.00 | 551 | 0.114288 |
| 90.15 | 445 | 0.092301 |
| 500.00 | 443 | 0.091886 |
| 160.00 | 425 | 0.088153 |
| 38.00 | 403 | 0.083590 |
| 85.00 | 387 | 0.080271 |
| 34.00 | 382 | 0.079234 |
| 52.00 | 375 | 0.077782 |
| 37.00 | 371 | 0.076952 |
| 400.00 | 342 | 0.070937 |
| 31.00 | 308 | 0.063885 |
| 170.00 | 306 | 0.063470 |
| 175.00 | 301 | 0.062433 |
| 84.00 | 277 | 0.057455 |
| 240.00 | 260 | 0.053929 |
| 72.12 | 241 | 0.049988 |
| 260.00 | 238 | 0.049366 |
| 29.00 | 237 | 0.049158 |
| 350.00 | 235 | 0.048743 |
| 210.00 | 216 | 0.044802 |
| 165.00 | 211 | 0.043765 |
| 600.00 | 194 | 0.040239 |
| 115.00 | 186 | 0.038580 |
| 36.06 | 182 | 0.037750 |
| 48.00 | 181 | 0.037543 |
| 46.00 | 181 | 0.037543 |
| 1000.00 | 179 | 0.037128 |
| 105.00 | 179 | 0.037128 |
| 120.20 | 171 | 0.035469 |
| 220.00 | 165 | 0.034224 |
| 43.00 | 164 | 0.034017 |
| 78.00 | 164 | 0.034017 |
| 62.00 | 157 | 0.032565 |
| 44.00 | 153 | 0.031735 |
| 56.00 | 146 | 0.030283 |
| 95.00 | 146 | 0.030283 |
| 150.25 | 144 | 0.029868 |
| 39.00 | 141 | 0.029246 |
| 230.00 | 137 | 0.028416 |
| 54.00 | 136 | 0.028209 |
| 41.00 | 135 | 0.028002 |
| 225.00 | 132 | 0.027379 |
| 34.85 | 132 | 0.027379 |
| 66.00 | 130 | 0.026964 |
| 135.00 | 120 | 0.024890 |
| 12.50 | 117 | 0.024268 |
| 190.00 | 107 | 0.022194 |
| 58.00 | 100 | 0.020742 |
| 9.01 | 91 | 0.018875 |
| 24.04 | 90 | 0.018668 |
| 450.00 | 90 | 0.018668 |
| 8.67 | 88 | 0.018253 |
| 155.00 | 86 | 0.017838 |
| 144.00 | 85 | 0.017631 |
| 61.00 | 82 | 0.017008 |
| 360.00 | 79 | 0.016386 |
| 47.00 | 78 | 0.016179 |
| 1.00 | 78 | 0.016179 |
| 15.02 | 76 | 0.015764 |
| 48.08 | 72 | 0.014934 |
| 63.00 | 71 | 0.014727 |
| 96.00 | 70 | 0.014519 |
| 270.00 | 65 | 0.013482 |
| 51.00 | 62 | 0.012860 |
| 53.00 | 58 | 0.012030 |
| 8.66 | 54 | 0.011201 |
| 320.00 | 53 | 0.010993 |
| 180.30 | 52 | 0.010786 |
| 68.00 | 51 | 0.010578 |
| 275.00 | 50 | 0.010371 |
| 145.00 | 50 | 0.010371 |
| 98.00 | 49 | 0.010164 |
| 74.00 | 48 | 0.009956 |
| 7.50 | 47 | 0.009749 |
| 82.00 | 47 | 0.009749 |
| 300.50 | 45 | 0.009334 |
| 57.00 | 44 | 0.009126 |
| 280.00 | 43 | 0.008919 |
| 375.00 | 43 | 0.008919 |
| 185.00 | 41 | 0.008504 |
| 330.00 | 40 | 0.008297 |
| 67.00 | 39 | 0.008089 |
| 91.00 | 39 | 0.008089 |
| 93.15 | 39 | 0.008089 |
| 112.00 | 39 | 0.008089 |
| 310.00 | 39 | 0.008089 |
| 550.00 | 39 | 0.008089 |
| 325.00 | 36 | 0.007467 |
| 700.00 | 36 | 0.007467 |
| 42.07 | 35 | 0.007260 |
| 390.00 | 35 | 0.007260 |
| 37.50 | 32 | 0.006637 |
| 800.00 | 31 | 0.006430 |
| 76.00 | 30 | 0.006223 |
| 2000.00 | 30 | 0.006223 |
| 6.02 | 30 | 0.006223 |
| 14.42 | 30 | 0.006223 |
| 2.00 | 28 | 0.005808 |
| 64.00 | 28 | 0.005808 |
| 5.33 | 25 | 0.005185 |
| 81.00 | 25 | 0.005185 |
| 132.00 | 25 | 0.005185 |
| 94.00 | 24 | 0.004978 |
| 54.09 | 24 | 0.004978 |
| 86.00 | 24 | 0.004978 |
| 92.00 | 24 | 0.004978 |
| 124.00 | 24 | 0.004978 |
| 73.00 | 23 | 0.004771 |
| 239.00 | 23 | 0.004771 |
| 6.33 | 23 | 0.004771 |
| 77.00 | 22 | 0.004563 |
| 28.84 | 22 | 0.004563 |
| 290.00 | 21 | 0.004356 |
| 156.00 | 21 | 0.004356 |
| 49.00 | 21 | 0.004356 |
| 1500.00 | 21 | 0.004356 |
| 420.00 | 20 | 0.004148 |
| 1200.00 | 19 | 0.003941 |
| 45.07 | 19 | 0.003941 |
| 102.00 | 19 | 0.003941 |
| 3000.00 | 18 | 0.003734 |
| 520.00 | 18 | 0.003734 |
| 30.12 | 17 | 0.003526 |
| 71.00 | 17 | 0.003526 |
| 59.00 | 17 | 0.003526 |
| 108.00 | 17 | 0.003526 |
| 83.00 | 17 | 0.003526 |
| 69.00 | 16 | 0.003319 |
| 122.00 | 16 | 0.003319 |
| 370.00 | 16 | 0.003319 |
| 235.00 | 16 | 0.003319 |
| 162.00 | 15 | 0.003111 |
| 365.00 | 15 | 0.003111 |
| 650.00 | 15 | 0.003111 |
| 215.00 | 15 | 0.003111 |
| 900.00 | 15 | 0.003111 |
| 601.01 | 14 | 0.002904 |
| 205.00 | 14 | 0.002904 |
| 104.00 | 13 | 0.002696 |
| 87.00 | 13 | 0.002696 |
| 195.00 | 13 | 0.002696 |
| 750.00 | 13 | 0.002696 |
| 152.00 | 12 | 0.002489 |
| 168.00 | 12 | 0.002489 |
| 144.24 | 12 | 0.002489 |
| 84.14 | 12 | 0.002489 |
| 126.00 | 12 | 0.002489 |
| 88.00 | 11 | 0.002282 |
| 340.00 | 11 | 0.002282 |
| 108.18 | 10 | 0.002074 |
| 21.03 | 10 | 0.002074 |
| 97.00 | 9 | 0.001867 |
| 106.00 | 9 | 0.001867 |
| 425.00 | 9 | 0.001867 |
| 240.40 | 9 | 0.001867 |
| 182.00 | 8 | 0.001659 |
| 9.12 | 8 | 0.001659 |
| 136.00 | 8 | 0.001659 |
| 380.00 | 8 | 0.001659 |
| 3.50 | 8 | 0.001659 |
| 33.05 | 8 | 0.001659 |
| 4.50 | 8 | 0.001659 |
| 148.00 | 8 | 0.001659 |
| 460.00 | 8 | 0.001659 |
| 224.00 | 8 | 0.001659 |
| 174.00 | 8 | 0.001659 |
| 78.13 | 8 | 0.001659 |
| 480.00 | 8 | 0.001659 |
| 8.50 | 7 | 0.001452 |
| 22.50 | 7 | 0.001452 |
| 60.24 | 7 | 0.001452 |
| 255.00 | 7 | 0.001452 |
| 134.00 | 7 | 0.001452 |
| 79.00 | 7 | 0.001452 |
| 93.00 | 7 | 0.001452 |
| 57.69 | 7 | 0.001452 |
| 5000.00 | 7 | 0.001452 |
| 116.00 | 7 | 0.001452 |
| 101.00 | 7 | 0.001452 |
| 6.50 | 7 | 0.001452 |
| 8.01 | 6 | 0.001245 |
| 360.60 | 6 | 0.001245 |
| 123.00 | 6 | 0.001245 |
| 40.05 | 6 | 0.001245 |
| 216.00 | 6 | 0.001245 |
| 300.51 | 6 | 0.001245 |
| 245.00 | 6 | 0.001245 |
| 410.00 | 6 | 0.001245 |
| 850.00 | 6 | 0.001245 |
| 17.50 | 6 | 0.001245 |
| 10.01 | 6 | 0.001245 |
| 142.00 | 5 | 0.001037 |
| 430.00 | 5 | 0.001037 |
| 32.50 | 5 | 0.001037 |
| 99.00 | 5 | 0.001037 |
| 315.00 | 5 | 0.001037 |
| 470.00 | 5 | 0.001037 |
| 90.36 | 5 | 0.001037 |
| 286.00 | 5 | 0.001037 |
| 324.00 | 5 | 0.001037 |
| 15.03 | 5 | 0.001037 |
| 210.35 | 5 | 0.001037 |
| 35.05 | 5 | 0.001037 |
| 620.00 | 5 | 0.001037 |
| 440.00 | 4 | 0.000830 |
| 166.00 | 4 | 0.000830 |
| 450.75 | 4 | 0.000830 |
| 285.00 | 4 | 0.000830 |
| 201.00 | 4 | 0.000830 |
| 3.60 | 4 | 0.000830 |
| 660.00 | 4 | 0.000830 |
| 184.00 | 4 | 0.000830 |
| 312.00 | 4 | 0.000830 |
| 27.04 | 4 | 0.000830 |
| 103.00 | 4 | 0.000830 |
| 114.00 | 4 | 0.000830 |
| 2500.00 | 4 | 0.000830 |
| 161.00 | 4 | 0.000830 |
| 118.00 | 4 | 0.000830 |
| 107.00 | 4 | 0.000830 |
| 212.00 | 4 | 0.000830 |
| 345.00 | 4 | 0.000830 |
| 1100.00 | 4 | 0.000830 |
| 236.00 | 4 | 0.000830 |
| 720.00 | 4 | 0.000830 |
| 192.00 | 4 | 0.000830 |
| 265.00 | 4 | 0.000830 |
| 117.00 | 4 | 0.000830 |
| 5.50 | 4 | 0.000830 |
| 15.20 | 4 | 0.000830 |
| 70.10 | 3 | 0.000622 |
| 1400.00 | 3 | 0.000622 |
| 89.00 | 3 | 0.000622 |
| 20.03 | 3 | 0.000622 |
| 202.00 | 3 | 0.000622 |
| 384.00 | 3 | 0.000622 |
| 6000.00 | 3 | 0.000622 |
| 151.00 | 3 | 0.000622 |
| 113.00 | 3 | 0.000622 |
| 11.50 | 3 | 0.000622 |
| 65.10 | 3 | 0.000622 |
| 121.00 | 3 | 0.000622 |
| 7.01 | 3 | 0.000622 |
| 154.00 | 3 | 0.000622 |
| 66.11 | 3 | 0.000622 |
| 22.53 | 3 | 0.000622 |
| 198.00 | 3 | 0.000622 |
| 10.50 | 3 | 0.000622 |
| 111.00 | 3 | 0.000622 |
| 305.00 | 3 | 0.000622 |
| 625.00 | 3 | 0.000622 |
| 177.00 | 3 | 0.000622 |
| 109.00 | 3 | 0.000622 |
| 131.00 | 3 | 0.000622 |
| 234.00 | 3 | 0.000622 |
| 222.00 | 3 | 0.000622 |
| 100.15 | 3 | 0.000622 |
| 137.00 | 3 | 0.000622 |
| 13.50 | 3 | 0.000622 |
| 133.00 | 3 | 0.000622 |
| 39.85 | 2 | 0.000415 |
| 7.77 | 2 | 0.000415 |
| 14.33 | 2 | 0.000415 |
| 4.20 | 2 | 0.000415 |
| 252.00 | 2 | 0.000415 |
| 45.05 | 2 | 0.000415 |
| 14.02 | 2 | 0.000415 |
| 7.21 | 2 | 0.000415 |
| 9.02 | 2 | 0.000415 |
| 189.00 | 2 | 0.000415 |
| 167.00 | 2 | 0.000415 |
| 346.00 | 2 | 0.000415 |
| 475.00 | 2 | 0.000415 |
| 36.66 | 2 | 0.000415 |
| 62.50 | 2 | 0.000415 |
| 36.05 | 2 | 0.000415 |
| 25000.00 | 2 | 0.000415 |
| 127.00 | 2 | 0.000415 |
| 725.00 | 2 | 0.000415 |
| 640.00 | 2 | 0.000415 |
| 173.00 | 2 | 0.000415 |
| 172.00 | 2 | 0.000415 |
| 157.00 | 2 | 0.000415 |
| 149.00 | 2 | 0.000415 |
| 416.00 | 2 | 0.000415 |
| 580.00 | 2 | 0.000415 |
| 16.50 | 2 | 0.000415 |
| 525.00 | 2 | 0.000415 |
| 153.00 | 2 | 0.000415 |
| 187.00 | 2 | 0.000415 |
| 204.00 | 2 | 0.000415 |
| 196.00 | 2 | 0.000415 |
| 143.00 | 2 | 0.000415 |
| 23.03 | 2 | 0.000415 |
| 540.00 | 2 | 0.000415 |
| 52.50 | 2 | 0.000415 |
| 24.50 | 2 | 0.000415 |
| 295.00 | 2 | 0.000415 |
| 138.00 | 2 | 0.000415 |
| 42.05 | 2 | 0.000415 |
| 560.00 | 2 | 0.000415 |
| 18.66 | 2 | 0.000415 |
| 18.06 | 2 | 0.000415 |
| 27.50 | 2 | 0.000415 |
| 194.00 | 2 | 0.000415 |
| 160.25 | 2 | 0.000415 |
| 7.60 | 1 | 0.000207 |
| 262.00 | 1 | 0.000207 |
| 478.00 | 1 | 0.000207 |
| 28.66 | 1 | 0.000207 |
| 20.33 | 1 | 0.000207 |
| 318.00 | 1 | 0.000207 |
| 56.25 | 1 | 0.000207 |
| 356.00 | 1 | 0.000207 |
| 468.00 | 1 | 0.000207 |
| 6.60 | 1 | 0.000207 |
| 14.50 | 1 | 0.000207 |
| 30.40 | 1 | 0.000207 |
| 446.00 | 1 | 0.000207 |
| 755.00 | 1 | 0.000207 |
| 242.00 | 1 | 0.000207 |
| 7.33 | 1 | 0.000207 |
| 96.16 | 1 | 0.000207 |
| 47.50 | 1 | 0.000207 |
| 40.02 | 1 | 0.000207 |
| 1300.00 | 1 | 0.000207 |
| 510.00 | 1 | 0.000207 |
| 128.00 | 1 | 0.000207 |
| 322.00 | 1 | 0.000207 |
| 244.00 | 1 | 0.000207 |
| 13.70 | 1 | 0.000207 |
| 256.00 | 1 | 0.000207 |
| 139.00 | 1 | 0.000207 |
| 820.00 | 1 | 0.000207 |
| 13.40 | 1 | 0.000207 |
| 333.00 | 1 | 0.000207 |
| 796.00 | 1 | 0.000207 |
| 323.00 | 1 | 0.000207 |
| 225.35 | 1 | 0.000207 |
| 214.00 | 1 | 0.000207 |
| 1360.00 | 1 | 0.000207 |
| 22.02 | 1 | 0.000207 |
| 1.20 | 1 | 0.000207 |
| 288.48 | 1 | 0.000207 |
| 34.12 | 1 | 0.000207 |
| 316.00 | 1 | 0.000207 |
| 21.50 | 1 | 0.000207 |
| 70.01 | 1 | 0.000207 |
| 505.00 | 1 | 0.000207 |
| 338.00 | 1 | 0.000207 |
| 13.02 | 1 | 0.000207 |
| 60.01 | 1 | 0.000207 |
| 1350.00 | 1 | 0.000207 |
| 120.10 | 1 | 0.000207 |
| 20.64 | 1 | 0.000207 |
| 90.75 | 1 | 0.000207 |
| 362.00 | 1 | 0.000207 |
| 52.88 | 1 | 0.000207 |
| 258.00 | 1 | 0.000207 |
| 138.15 | 1 | 0.000207 |
| 63.10 | 1 | 0.000207 |
| 31.05 | 1 | 0.000207 |
| 3.20 | 1 | 0.000207 |
| 138.23 | 1 | 0.000207 |
| 950.00 | 1 | 0.000207 |
| 60.05 | 1 | 0.000207 |
| 462.00 | 1 | 0.000207 |
| 1120.00 | 1 | 0.000207 |
| 224.24 | 1 | 0.000207 |
| 147.00 | 1 | 0.000207 |
| 218.00 | 1 | 0.000207 |
| 25.24 | 1 | 0.000207 |
| 16.25 | 1 | 0.000207 |
| 278.80 | 1 | 0.000207 |
| 129.00 | 1 | 0.000207 |
| 203.00 | 1 | 0.000207 |
| 34.84 | 1 | 0.000207 |
| 1803.03 | 1 | 0.000207 |
| 7.51 | 1 | 0.000207 |
| 570.00 | 1 | 0.000207 |
| 11.01 | 1 | 0.000207 |
| 710.00 | 1 | 0.000207 |
| 675.00 | 1 | 0.000207 |
| 70.25 | 1 | 0.000207 |
| 100.10 | 1 | 0.000207 |
| 6.25 | 1 | 0.000207 |
| 504.00 | 1 | 0.000207 |
| 17.34 | 1 | 0.000207 |
| 15.67 | 1 | 0.000207 |
| 860.00 | 1 | 0.000207 |
| 735.00 | 1 | 0.000207 |
| 183.00 | 1 | 0.000207 |
| 206.14 | 1 | 0.000207 |
| 10.57 | 1 | 0.000207 |
| 39.99 | 1 | 0.000207 |
| 6.61 | 1 | 0.000207 |
| 38.06 | 1 | 0.000207 |
| 114.15 | 1 | 0.000207 |
| 595.00 | 1 | 0.000207 |
| 243.00 | 1 | 0.000207 |
| 2250.00 | 1 | 0.000207 |
| 2400.00 | 1 | 0.000207 |
| 60.15 | 1 | 0.000207 |
| 313.00 | 1 | 0.000207 |
| 385.00 | 1 | 0.000207 |
| 150.15 | 1 | 0.000207 |
| 66.50 | 1 | 0.000207 |
| 81.10 | 1 | 0.000207 |
| 335.00 | 1 | 0.000207 |
| 304.00 | 1 | 0.000207 |
| 19.03 | 1 | 0.000207 |
| 2.99 | 1 | 0.000207 |
| 64.90 | 1 | 0.000207 |
| 451.00 | 1 | 0.000207 |
| 20.83 | 1 | 0.000207 |
| 455.00 | 1 | 0.000207 |
| 44.06 | 1 | 0.000207 |
| 185.25 | 1 | 0.000207 |
| 6.31 | 1 | 0.000207 |
| 30.50 | 1 | 0.000207 |
| 39.01 | 1 | 0.000207 |
| 306.51 | 1 | 0.000207 |
| 13.22 | 1 | 0.000207 |
| 126.15 | 1 | 0.000207 |
| 102.17 | 1 | 0.000207 |
| 15000.00 | 1 | 0.000207 |
| 450.50 | 1 | 0.000207 |
| 320.50 | 1 | 0.000207 |
| 59.02 | 1 | 0.000207 |
| 112.50 | 1 | 0.000207 |
| 278.00 | 1 | 0.000207 |
| 530.00 | 1 | 0.000207 |
| 90.10 | 1 | 0.000207 |
| 182.50 | 1 | 0.000207 |
| 28.34 | 1 | 0.000207 |
| 75.10 | 1 | 0.000207 |
| 146.00 | 1 | 0.000207 |
| 576.20 | 1 | 0.000207 |
| 125.20 | 1 | 0.000207 |
| 180.20 | 1 | 0.000207 |
| 5.30 | 1 | 0.000207 |
| 186.00 | 1 | 0.000207 |
| 6.66 | 1 | 0.000207 |
| 207.00 | 1 | 0.000207 |
| 11.66 | 1 | 0.000207 |
| 23.50 | 1 | 0.000207 |
| 71.96 | 1 | 0.000207 |
| 228.00 | 1 | 0.000207 |
| 565.00 | 1 | 0.000207 |
| 65.86 | 1 | 0.000207 |
| 16.67 | 1 | 0.000207 |
| 70.24 | 1 | 0.000207 |
| 272.00 | 1 | 0.000207 |
| 306.00 | 1 | 0.000207 |
| 10.25 | 1 | 0.000207 |
| 216.36 | 1 | 0.000207 |
| 1250.00 | 1 | 0.000207 |
| 67.50 | 1 | 0.000207 |
| 717.00 | 1 | 0.000207 |
| 302.00 | 1 | 0.000207 |
| 7.25 | 1 | 0.000207 |
| 10.33 | 1 | 0.000207 |
| 2.50 | 1 | 0.000207 |
| 171.00 | 1 | 0.000207 |
| 17.73 | 1 | 0.000207 |
| 20.01 | 1 | 0.000207 |
| 248.00 | 1 | 0.000207 |
| 33.33 | 1 | 0.000207 |
| 52.58 | 1 | 0.000207 |
| 4.66 | 1 | 0.000207 |
| 1502.53 | 1 | 0.000207 |
| 585.00 | 1 | 0.000207 |
| 136.50 | 1 | 0.000207 |
| 193.00 | 1 | 0.000207 |
| 181.00 | 1 | 0.000207 |
| 289.00 | 1 | 0.000207 |
| 382.00 | 1 | 0.000207 |
| 253.00 | 1 | 0.000207 |
| 112.12 | 1 | 0.000207 |
| 9.50 | 1 | 0.000207 |
| 4.63 | 1 | 0.000207 |
| 13.33 | 1 | 0.000207 |
| 9.61 | 1 | 0.000207 |
| 740.00 | 1 | 0.000207 |
| 7.05 | 1 | 0.000207 |
| 274.00 | 1 | 0.000207 |
| 50.40 | 1 | 0.000207 |
| 16.60 | 1 | 0.000207 |
| 790.00 | 1 | 0.000207 |
| 576.00 | 1 | 0.000207 |
| 8000.00 | 1 | 0.000207 |
| 159.00 | 1 | 0.000207 |
| 241.00 | 1 | 0.000207 |
| 4.99 | 1 | 0.000207 |
| 875.00 | 1 | 0.000207 |
| 590.00 | 1 | 0.000207 |
| 16.53 | 1 | 0.000207 |
| 3.33 | 1 | 0.000207 |
| 8.30 | 1 | 0.000207 |
| 8.33 | 1 | 0.000207 |
| 602.00 | 1 | 0.000207 |
| 252.36 | 1 | 0.000207 |
| 1442.43 | 1 | 0.000207 |
| 337.00 | 1 | 0.000207 |
| 158.00 | 1 | 0.000207 |
| 229.00 | 1 | 0.000207 |
| 75.12 | 1 | 0.000207 |
| 1750.00 | 1 | 0.000207 |
| 19.50 | 1 | 0.000207 |
| 3600.00 | 1 | 0.000207 |
| 77.10 | 1 | 0.000207 |
| 1620.00 | 1 | 0.000207 |
| 10.05 | 1 | 0.000207 |
| 41.06 | 1 | 0.000207 |
| 37.06 | 1 | 0.000207 |
| 169.00 | 1 | 0.000207 |
| 232.00 | 1 | 0.000207 |
| 164.00 | 1 | 0.000207 |
| 31.84 | 1 | 0.000207 |
| 9.20 | 1 | 0.000207 |
| 8.41 | 1 | 0.000207 |
| 32.05 | 1 | 0.000207 |
| 40.06 | 1 | 0.000207 |
| 415.00 | 1 | 0.000207 |
| 175.25 | 1 | 0.000207 |
| 116.66 | 1 | 0.000207 |
| 20.43 | 1 | 0.000207 |
| 19.83 | 1 | 0.000207 |
| 1202.02 | 1 | 0.000207 |
| 7.81 | 1 | 0.000207 |
| 311.00 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__last_soft_credit_amount__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npsp__last_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquotachange__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_annualizedquotachange__c es 5244. Lo que supone un 1.0874614287136268% El nº de vacios para la variable msf_annualizedquotachange__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 48.00 | 179597 | 37.652941 |
| 72.00 | 41191 | 8.635792 |
| 60.00 | 39853 | 8.355277 |
| 24.00 | 36904 | 7.737012 |
| 120.00 | 25986 | 5.448027 |
| 36.00 | 23779 | 4.985324 |
| 84.00 | 20851 | 4.371462 |
| 50.00 | 9979 | 2.092121 |
| 30.00 | 7005 | 1.468615 |
| 144.00 | 6938 | 1.454568 |
| 40.00 | 5109 | 1.071114 |
| 25.00 | 4819 | 1.010315 |
| 45.00 | 4380 | 0.918277 |
| 108.00 | 3631 | 0.761248 |
| 20.00 | 3500 | 0.733783 |
| 15.00 | 3187 | 0.668162 |
| 28.00 | 3157 | 0.661873 |
| 10.00 | 2964 | 0.621410 |
| 64.00 | 2834 | 0.594155 |
| 35.88 | 2638 | 0.553063 |
| 96.00 | 2623 | 0.549918 |
| 35.00 | 2397 | 0.502537 |
| 12.00 | 1940 | 0.406726 |
| 100.00 | 1761 | 0.369198 |
| 70.00 | 1679 | 0.352006 |
| 20.04 | 1517 | 0.318043 |
| 52.00 | 1511 | 0.316785 |
| 8.00 | 1479 | 0.310076 |
| 7.00 | 1476 | 0.309447 |
| 5.00 | 1469 | 0.307979 |
| 90.00 | 1401 | 0.293723 |
| 56.00 | 1357 | 0.284498 |
| 240.00 | 1239 | 0.259759 |
| 29.90 | 1135 | 0.237955 |
| 6.00 | 1109 | 0.232505 |
| 2.00 | 1033 | 0.216571 |
| 47.80 | 944 | 0.197912 |
| 18.00 | 941 | 0.197283 |
| 80.00 | 914 | 0.191622 |
| 55.00 | 893 | 0.187220 |
| 132.00 | 831 | 0.174221 |
| 14.95 | 809 | 0.169609 |
| 88.00 | 715 | 0.149901 |
| 16.00 | 709 | 0.148644 |
| 44.00 | 668 | 0.140048 |
| 47.76 | 632 | 0.132500 |
| 180.00 | 603 | 0.126420 |
| 168.00 | 598 | 0.125372 |
| 65.00 | 584 | 0.122437 |
| 32.00 | 548 | 0.114890 |
| 76.00 | 548 | 0.114890 |
| 119.40 | 502 | 0.105246 |
| 17.00 | 457 | 0.095811 |
| 22.00 | 456 | 0.095601 |
| 14.00 | 436 | 0.091408 |
| 59.64 | 434 | 0.090989 |
| 0.00 | 421 | 0.088264 |
| 33.00 | 414 | 0.086796 |
| 44.85 | 373 | 0.078200 |
| 140.00 | 367 | 0.076942 |
| 54.00 | 362 | 0.075894 |
| 42.00 | 357 | 0.074846 |
| 192.00 | 346 | 0.072540 |
| 71.60 | 321 | 0.067298 |
| 27.00 | 296 | 0.062057 |
| 156.00 | 294 | 0.061638 |
| 200.00 | 289 | 0.060590 |
| 160.00 | 260 | 0.054510 |
| 4.00 | 249 | 0.052203 |
| 34.00 | 244 | 0.051155 |
| 32.88 | 223 | 0.046752 |
| 8.97 | 193 | 0.040463 |
| 68.00 | 181 | 0.037947 |
| 11.00 | 180 | 0.037737 |
| 21.00 | 172 | 0.036060 |
| 360.00 | 171 | 0.035851 |
| 41.00 | 171 | 0.035851 |
| 44.80 | 157 | 0.032915 |
| 9.00 | 156 | 0.032706 |
| 110.00 | 141 | 0.029561 |
| 23.92 | 135 | 0.028303 |
| 59.75 | 127 | 0.026626 |
| 130.00 | 127 | 0.026626 |
| 142.80 | 123 | 0.025787 |
| 300.00 | 123 | 0.025787 |
| 31.00 | 114 | 0.023900 |
| 480.00 | 105 | 0.022014 |
| 55.76 | 103 | 0.021594 |
| 26.00 | 96 | 0.020127 |
| 51.96 | 90 | 0.018869 |
| 58.00 | 89 | 0.018659 |
| 3.00 | 88 | 0.018449 |
| 19.00 | 85 | 0.017820 |
| 47.88 | 83 | 0.017401 |
| 62.00 | 81 | 0.016982 |
| 99.40 | 78 | 0.016353 |
| 17.94 | 76 | 0.015934 |
| 38.00 | 74 | 0.015514 |
| 17.15 | 69 | 0.014466 |
| 11.96 | 63 | 0.013208 |
| 75.00 | 63 | 0.013208 |
| 104.00 | 58 | 0.012160 |
| 49.70 | 56 | 0.011741 |
| 40.08 | 56 | 0.011741 |
| 46.00 | 56 | 0.011741 |
| 52.60 | 51 | 0.010692 |
| 47.84 | 51 | 0.010692 |
| 105.00 | 46 | 0.009644 |
| 89.50 | 44 | 0.009225 |
| 83.52 | 42 | 0.008805 |
| 53.00 | 41 | 0.008596 |
| 400.00 | 39 | 0.008176 |
| 66.00 | 37 | 0.007757 |
| 13.00 | 36 | 0.007547 |
| 47.00 | 34 | 0.007128 |
| 46.85 | 34 | 0.007128 |
| 600.00 | 33 | 0.006919 |
| 37.00 | 33 | 0.006919 |
| 35.76 | 31 | 0.006499 |
| 5.98 | 30 | 0.006290 |
| 95.00 | 30 | 0.006290 |
| 720.00 | 27 | 0.005661 |
| 43.00 | 25 | 0.005241 |
| 150.00 | 24 | 0.005032 |
| 32.04 | 24 | 0.005032 |
| 51.00 | 23 | 0.004822 |
| 49.00 | 22 | 0.004612 |
| 119.00 | 22 | 0.004612 |
| 2.99 | 22 | 0.004612 |
| 66.96 | 21 | 0.004403 |
| 119.28 | 20 | 0.004193 |
| 63.72 | 20 | 0.004193 |
| 55.68 | 19 | 0.003983 |
| 178.20 | 19 | 0.003983 |
| 15.96 | 19 | 0.003983 |
| 125.00 | 18 | 0.003774 |
| 78.00 | 18 | 0.003774 |
| 139.20 | 17 | 0.003564 |
| 27.92 | 17 | 0.003564 |
| 1200.00 | 16 | 0.003354 |
| 118.99 | 16 | 0.003354 |
| 320.00 | 16 | 0.003354 |
| 92.00 | 16 | 0.003354 |
| 112.00 | 15 | 0.003145 |
| 85.00 | 15 | 0.003145 |
| 51.72 | 14 | 0.002935 |
| 63.64 | 14 | 0.002935 |
| 228.00 | 13 | 0.002725 |
| 59.00 | 12 | 0.002516 |
| 121.80 | 12 | 0.002516 |
| 26.91 | 12 | 0.002516 |
| 40.86 | 12 | 0.002516 |
| 107.40 | 11 | 0.002306 |
| 107.04 | 11 | 0.002306 |
| 166.56 | 10 | 0.002097 |
| 14.16 | 10 | 0.002097 |
| 280.00 | 10 | 0.002097 |
| 57.00 | 10 | 0.002097 |
| 118.56 | 10 | 0.002097 |
| 51.82 | 9 | 0.001887 |
| 36.87 | 9 | 0.001887 |
| 69.60 | 9 | 0.001887 |
| 56.64 | 8 | 0.001677 |
| 39.00 | 8 | 0.001677 |
| 20.93 | 8 | 0.001677 |
| 46.56 | 8 | 0.001677 |
| 124.00 | 8 | 0.001677 |
| 237.60 | 7 | 0.001468 |
| 23.00 | 7 | 0.001468 |
| 71.64 | 7 | 0.001468 |
| 95.16 | 7 | 0.001468 |
| 29.76 | 6 | 0.001258 |
| 28.31 | 6 | 0.001258 |
| 39.88 | 6 | 0.001258 |
| 45.60 | 6 | 0.001258 |
| 216.00 | 6 | 0.001258 |
| 420.00 | 6 | 0.001258 |
| 29.00 | 6 | 0.001258 |
| 204.00 | 6 | 0.001258 |
| 26.32 | 5 | 0.001048 |
| 51.80 | 5 | 0.001048 |
| 357.00 | 5 | 0.001048 |
| 1440.00 | 5 | 0.001048 |
| 47.52 | 5 | 0.001048 |
| 960.00 | 5 | 0.001048 |
| 94.00 | 5 | 0.001048 |
| 276.00 | 5 | 0.001048 |
| 238.00 | 4 | 0.000839 |
| 51.84 | 4 | 0.000839 |
| 19.95 | 4 | 0.000839 |
| 97.92 | 4 | 0.000839 |
| 21.93 | 4 | 0.000839 |
| 6.58 | 4 | 0.000839 |
| 47.64 | 4 | 0.000839 |
| 1000.00 | 4 | 0.000839 |
| 114.00 | 4 | 0.000839 |
| 44.64 | 4 | 0.000839 |
| 116.00 | 4 | 0.000839 |
| 59.65 | 4 | 0.000839 |
| 135.00 | 4 | 0.000839 |
| 67.00 | 4 | 0.000839 |
| 220.00 | 4 | 0.000839 |
| 74.00 | 4 | 0.000839 |
| 63.00 | 4 | 0.000839 |
| 89.49 | 4 | 0.000839 |
| 41.88 | 4 | 0.000839 |
| 126.00 | 4 | 0.000839 |
| 115.00 | 4 | 0.000839 |
| 49.85 | 3 | 0.000629 |
| 33.89 | 3 | 0.000629 |
| 2400.00 | 3 | 0.000629 |
| 260.00 | 3 | 0.000629 |
| 21.60 | 3 | 0.000629 |
| 59.76 | 3 | 0.000629 |
| 16.95 | 3 | 0.000629 |
| 129.25 | 3 | 0.000629 |
| 128.00 | 3 | 0.000629 |
| 136.00 | 3 | 0.000629 |
| 800.00 | 3 | 0.000629 |
| 68.04 | 3 | 0.000629 |
| 82.00 | 3 | 0.000629 |
| 106.00 | 3 | 0.000629 |
| 16.80 | 3 | 0.000629 |
| 79.50 | 3 | 0.000629 |
| 148.00 | 3 | 0.000629 |
| 14.88 | 3 | 0.000629 |
| 118.80 | 2 | 0.000419 |
| 288.00 | 2 | 0.000419 |
| 3.99 | 2 | 0.000419 |
| 500.00 | 2 | 0.000419 |
| 75.60 | 2 | 0.000419 |
| 44.90 | 2 | 0.000419 |
| 17.34 | 2 | 0.000419 |
| 33.48 | 2 | 0.000419 |
| 31.90 | 2 | 0.000419 |
| 162.00 | 2 | 0.000419 |
| 324.00 | 2 | 0.000419 |
| 145.00 | 2 | 0.000419 |
| 64.08 | 2 | 0.000419 |
| 43.88 | 2 | 0.000419 |
| 24.12 | 2 | 0.000419 |
| 13.96 | 2 | 0.000419 |
| 356.40 | 2 | 0.000419 |
| 3.34 | 2 | 0.000419 |
| 6.68 | 2 | 0.000419 |
| 38.76 | 2 | 0.000419 |
| 102.00 | 2 | 0.000419 |
| 25.04 | 2 | 0.000419 |
| 83.00 | 2 | 0.000419 |
| 540.00 | 2 | 0.000419 |
| 60.60 | 2 | 0.000419 |
| 780.00 | 2 | 0.000419 |
| 34.90 | 2 | 0.000419 |
| 20.95 | 2 | 0.000419 |
| 44.04 | 2 | 0.000419 |
| 714.00 | 2 | 0.000419 |
| 122.40 | 1 | 0.000210 |
| 154.00 | 1 | 0.000210 |
| 83.60 | 1 | 0.000210 |
| 13.60 | 1 | 0.000210 |
| 115.20 | 1 | 0.000210 |
| 119.20 | 1 | 0.000210 |
| 141.12 | 1 | 0.000210 |
| 264.00 | 1 | 0.000210 |
| 44.40 | 1 | 0.000210 |
| 20.40 | 1 | 0.000210 |
| 59.80 | 1 | 0.000210 |
| 86.00 | 1 | 0.000210 |
| 713.88 | 1 | 0.000210 |
| 28.80 | 1 | 0.000210 |
| 100.08 | 1 | 0.000210 |
| 59.28 | 1 | 0.000210 |
| 63.60 | 1 | 0.000210 |
| 384.00 | 1 | 0.000210 |
| 39.60 | 1 | 0.000210 |
| 4.50 | 1 | 0.000210 |
| 43.84 | 1 | 0.000210 |
| 143.76 | 1 | 0.000210 |
| 840.00 | 1 | 0.000210 |
| 28.68 | 1 | 0.000210 |
| 440.00 | 1 | 0.000210 |
| 51.77 | 1 | 0.000210 |
| 29.95 | 1 | 0.000210 |
| 65.76 | 1 | 0.000210 |
| 3600.00 | 1 | 0.000210 |
| 81.12 | 1 | 0.000210 |
| 138.00 | 1 | 0.000210 |
| 51.85 | 1 | 0.000210 |
| 237.96 | 1 | 0.000210 |
| 202.20 | 1 | 0.000210 |
| 190.80 | 1 | 0.000210 |
| 43.86 | 1 | 0.000210 |
| 64.32 | 1 | 0.000210 |
| 5.50 | 1 | 0.000210 |
| 29.85 | 1 | 0.000210 |
| 560.00 | 1 | 0.000210 |
| 6.98 | 1 | 0.000210 |
| 55.92 | 1 | 0.000210 |
| 87.00 | 1 | 0.000210 |
| 580.00 | 1 | 0.000210 |
| 97.00 | 1 | 0.000210 |
| 59.70 | 1 | 0.000210 |
| 60.48 | 1 | 0.000210 |
| 8.66 | 1 | 0.000210 |
| 44.25 | 1 | 0.000210 |
| 49.90 | 1 | 0.000210 |
| 296.97 | 1 | 0.000210 |
| 52.64 | 1 | 0.000210 |
| 135.44 | 1 | 0.000210 |
| 64.65 | 1 | 0.000210 |
| 27.88 | 1 | 0.000210 |
| 41.16 | 1 | 0.000210 |
| 60.04 | 1 | 0.000210 |
| 17.95 | 1 | 0.000210 |
| 109.25 | 1 | 0.000210 |
| 52.78 | 1 | 0.000210 |
| 900.00 | 1 | 0.000210 |
| 59.85 | 1 | 0.000210 |
| 29.99 | 1 | 0.000210 |
| 277.60 | 1 | 0.000210 |
| 34.99 | 1 | 0.000210 |
| 475.20 | 1 | 0.000210 |
| 81.52 | 1 | 0.000210 |
| 71.76 | 1 | 0.000210 |
| 53.64 | 1 | 0.000210 |
| 16.44 | 1 | 0.000210 |
| 640.00 | 1 | 0.000210 |
| 432.00 | 1 | 0.000210 |
| 28.98 | 1 | 0.000210 |
| 127.28 | 1 | 0.000210 |
| 15.92 | 1 | 0.000210 |
| 91.92 | 1 | 0.000210 |
| 178.80 | 1 | 0.000210 |
| 348.00 | 1 | 0.000210 |
| 250.00 | 1 | 0.000210 |
| 16.08 | 1 | 0.000210 |
| 179.00 | 1 | 0.000210 |
| 297.47 | 1 | 0.000210 |
| 166.68 | 1 | 0.000210 |
| 165.12 | 1 | 0.000210 |
| 55.80 | 1 | 0.000210 |
| 77.00 | 1 | 0.000210 |
| 53.82 | 1 | 0.000210 |
| 122.00 | 1 | 0.000210 |
| 98.56 | 1 | 0.000210 |
| 47.83 | 1 | 0.000210 |
| 350.00 | 1 | 0.000210 |
| 74.04 | 1 | 0.000210 |
| 5.60 | 1 | 0.000210 |
| 297.60 | 1 | 0.000210 |
| 103.44 | 1 | 0.000210 |
| 32.10 | 1 | 0.000210 |
| 61.68 | 1 | 0.000210 |
| 129.00 | 1 | 0.000210 |
| 50.68 | 1 | 0.000210 |
| 67.76 | 1 | 0.000210 |
| 48.85 | 1 | 0.000210 |
| 139.00 | 1 | 0.000210 |
| 32.28 | 1 | 0.000210 |
| 131.88 | 1 | 0.000210 |
| 83.49 | 1 | 0.000210 |
| 71.88 | 1 | 0.000210 |
| 57.36 | 1 | 0.000210 |
| 63.24 | 1 | 0.000210 |
| 87.52 | 1 | 0.000210 |
| 10.50 | 1 | 0.000210 |
| 69.00 | 1 | 0.000210 |
| 143.40 | 1 | 0.000210 |
| 236.00 | 1 | 0.000210 |
| 32.14 | 1 | 0.000210 |
| 81.00 | 1 | 0.000210 |
| 52.88 | 1 | 0.000210 |
| 56.04 | 1 | 0.000210 |
| 52.68 | 1 | 0.000210 |
| 35.40 | 1 | 0.000210 |
| 24.60 | 1 | 0.000210 |
| 67.64 | 1 | 0.000210 |
| 33.04 | 1 | 0.000210 |
| 660.00 | 1 | 0.000210 |
| 52.80 | 1 | 0.000210 |
| 52.08 | 1 | 0.000210 |
| 28.08 | 1 | 0.000210 |
| 1600.00 | 1 | 0.000210 |
| 372.00 | 1 | 0.000210 |
| 158.60 | 1 | 0.000210 |
| 257.57 | 1 | 0.000210 |
| 700.00 | 1 | 0.000210 |
| 15.95 | 1 | 0.000210 |
| 37.90 | 1 | 0.000210 |
| 19.80 | 1 | 0.000210 |
| 63.76 | 1 | 0.000210 |
| 61.00 | 1 | 0.000210 |
| 63.80 | 1 | 0.000210 |
| 109.92 | 1 | 0.000210 |
| 43.08 | 1 | 0.000210 |
| 147.72 | 1 | 0.000210 |
| 38.28 | 1 | 0.000210 |
| 19.94 | 1 | 0.000210 |
| 64.75 | 1 | 0.000210 |
| 133.36 | 1 | 0.000210 |
| 1320.00 | 1 | 0.000210 |
| 66.84 | 1 | 0.000210 |
| 40.68 | 1 | 0.000210 |
| 197.98 | 1 | 0.000210 |
| 89.00 | 1 | 0.000210 |
# Vamos a realizar analisis por cada variable
var = "msf_relationshiplevel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_relationshiplevel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_relationshiplevel__c es 4. Lo que supone un 0.0008294900295298452%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| a0l0O00000k727RQAQ | 459558 | 95.299695 |
| a0l0O00000k727SQAQ | 17380 | 3.604134 |
| a0l0O00000k727TQAQ | 4988 | 1.034374 |
| a0l0O00000k727UQAQ | 169 | 0.035046 |
| a0l0O00000k727QQAQ | 125 | 0.025922 |
| 4 | 0.000829 |
# Vamos a realizar analisis por cada variable
var = "msf_ltvcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_ltvcont__c es 825. Lo que supone un 0.17108231859053055% El nº de vacios para la variable msf_ltvcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 60.00 | 2118 | 0.439968 |
| 120.00 | 1991 | 0.413586 |
| 600.00 | 1773 | 0.368302 |
| 300.00 | 1769 | 0.367471 |
| 240.00 | 1660 | 0.344828 |
| ... | ... | ... |
| 3710.16 | 1 | 0.000208 |
| 2941.45 | 1 | 0.000208 |
| 426.21 | 1 | 0.000208 |
| 10741.52 | 1 | 0.000208 |
| 1628.70 | 1 | 0.000208 |
58883 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_ltvdesc__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_ltvdesc__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_ltvdesc__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Alto 1.000 - 3.000 | 186746 | 38.725986 |
| Alto 500 - 1.000 | 102130 | 21.178954 |
| Muy Alto 3.000 - 10.000 | 72328 | 14.998839 |
| Medio 180 - 500 | 69018 | 14.312436 |
| Bajo 120 - 180 | 13592 | 2.818607 |
| Muy bajo 0,10 - 50 | 13482 | 2.795796 |
| Muy bajo 50 - 100 | 12686 | 2.630728 |
| 10.000+ | 7412 | 1.537045 |
| Muy bajo 100 - 120 | 4005 | 0.830527 |
| Nulo | 825 | 0.171082 |
# Vamos a realizar analisis por cada variable
var = "mailingstate"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable mailingstate es 0. Lo que supone un 0.0% El nº de vacios para la variable mailingstate es 13102. Lo que supone un 2.7169945917250073%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| MADRID | 80494 | 16.692243 |
| BARCELONA | 52525 | 10.892241 |
| VALENCIA/VALÈNCIA | 24584 | 5.098046 |
| BIZKAIA | 21315 | 4.420145 |
| GIPUZKOA | 13693 | 2.839552 |
| SEVILLA | 13552 | 2.810312 |
| ALICANTE/ALACANT | 13164 | 2.729852 |
| A CORUÑA | 13121 | 2.720935 |
| 13102 | 2.716995 | |
| MÁLAGA | 12957 | 2.686926 |
| ILLES BALEARS | 10427 | 2.162273 |
| PONTEVEDRA | 10233 | 2.122043 |
| Madrid | 9475 | 1.964855 |
| ASTURIAS | 9413 | 1.951997 |
| MURCIA | 9085 | 1.883979 |
| CÁDIZ | 7584 | 1.572713 |
| Barcelona | 7116 | 1.475663 |
| ZARAGOZA | 6807 | 1.411585 |
| SANTA CRUZ DE TENERIFE | 6518 | 1.351654 |
| GRANADA | 6473 | 1.342322 |
| LAS PALMAS | 6123 | 1.269742 |
| NAVARRA | 6087 | 1.262276 |
| CANTABRIA | 5514 | 1.143452 |
| ARABA/ÁLAVA | 5306 | 1.100319 |
| VALLADOLID | 5236 | 1.085802 |
| GIRONA | 5048 | 1.046816 |
| TARRAGONA | 4371 | 0.906425 |
| CASTELLÓN/CASTELLÓ | 4279 | 0.887347 |
| LEÓN | 3533 | 0.732647 |
| CÓRDOBA | 3425 | 0.710251 |
| TOLEDO | 3204 | 0.664422 |
| BURGOS | 3037 | 0.629790 |
| HUELVA | 2917 | 0.604906 |
| Alicante/Alacant | 2872 | 0.595574 |
| Vizcaya | 2859 | 0.592878 |
| BADAJOZ | 2843 | 0.589560 |
| CIUDAD REAL | 2615 | 0.542279 |
| Sevilla | 2579 | 0.534814 |
| LA RIOJA | 2459 | 0.509929 |
| LLEIDA | 2424 | 0.502671 |
| Murcia | 2397 | 0.497072 |
| JAÉN | 2282 | 0.473224 |
| ALMERÍA | 2173 | 0.450620 |
| SALAMANCA | 2140 | 0.443777 |
| GUADALAJARA | 2079 | 0.431127 |
| CÁCERES | 2002 | 0.415160 |
| OURENSE | 1945 | 0.403340 |
| LUGO | 1909 | 0.395874 |
| A Coruña | 1886 | 0.391105 |
| Malaga | 1877 | 0.389238 |
| ALBACETE | 1857 | 0.385091 |
| Valencia | 1820 | 0.377418 |
| Valencia/Valencia | 1781 | 0.369330 |
| Granada | 1611 | 0.334077 |
| Cadiz | 1594 | 0.330552 |
| HUESCA | 1587 | 0.329100 |
| SEGOVIA | 1269 | 0.263156 |
| Alicante | 1195 | 0.247810 |
| PALENCIA | 1132 | 0.234746 |
| ÁVILA | 997 | 0.206750 |
| CUENCA | 996 | 0.206543 |
| ZAMORA | 983 | 0.203847 |
| Málaga | 915 | 0.189746 |
| Guipuzcoa | 910 | 0.188709 |
| SORIA | 842 | 0.174608 |
| Santa Cruz de Tenerife | 841 | 0.174400 |
| Pontevedra | 816 | 0.169216 |
| Badajoz | 816 | 0.169216 |
| Asturias | 798 | 0.165483 |
| TERUEL | 750 | 0.155529 |
| Cádiz | 652 | 0.135207 |
| VALENCIA | 636 | 0.131889 |
| Las Palmas | 614 | 0.127327 |
| Illes Balears | 614 | 0.127327 |
| Navarra | 600 | 0.124424 |
| Bizkaia | 561 | 0.116336 |
| Zaragoza | 557 | 0.115506 |
| Tarragona | 526 | 0.109078 |
| Cantabria | 517 | 0.107212 |
| Girona | 501 | 0.103894 |
| Salamanca | 488 | 0.101198 |
| Valladolid | 441 | 0.091451 |
| MALAGA | 441 | 0.091451 |
| Huelva | 432 | 0.089585 |
| Valencia/València | 400 | 0.082949 |
| Almeria | 384 | 0.079631 |
| ALICANTE | 361 | 0.074861 |
| Santa Cruz De Tenerife | 345 | 0.071544 |
| Baleares | 344 | 0.071336 |
| Toledo | 332 | 0.068848 |
| Ciudad Real | 332 | 0.068848 |
| Guipúzcoa | 301 | 0.062419 |
| Gipuzkoa | 300 | 0.062212 |
| MELILLA | 300 | 0.062212 |
| Burgos | 283 | 0.058686 |
| CEUTA | 262 | 0.054332 |
| Lleida | 246 | 0.051014 |
| Albacete | 239 | 0.049562 |
| VIZCAYA | 237 | 0.049147 |
| Almería | 228 | 0.047281 |
| Lugo | 223 | 0.046244 |
| Guadalajara | 223 | 0.046244 |
| CADIZ | 216 | 0.044792 |
| La Rioja | 215 | 0.044585 |
| Cordoba | 201 | 0.041682 |
| Valencia/Valéncia | 197 | 0.040852 |
| Caceres | 175 | 0.036290 |
| Ourense | 173 | 0.035875 |
| Córdoba | 169 | 0.035046 |
| Castellon/Castello | 167 | 0.034631 |
| León | 167 | 0.034631 |
| Leon | 158 | 0.032765 |
| Jaen | 156 | 0.032350 |
| Huesca | 154 | 0.031935 |
| Segovia | 148 | 0.030691 |
| Jaén | 143 | 0.029654 |
| Castellon | 142 | 0.029447 |
| alava | 130 | 0.026958 |
| Castellón | 127 | 0.026336 |
| Cáceres | 124 | 0.025714 |
| Zamora | 120 | 0.024885 |
| GUIPUZCOA | 114 | 0.023640 |
| Alacant | 107 | 0.022189 |
| Álava | 106 | 0.021981 |
| CORDOBA | 104 | 0.021567 |
| Alava | 104 | 0.021567 |
| València | 96 | 0.019908 |
| CASTELLON | 95 | 0.019700 |
| LEON | 93 | 0.019286 |
| Cuenca | 92 | 0.019078 |
| Palencia | 88 | 0.018249 |
| Castellón/Castelló | 87 | 0.018041 |
| JAEN | 73 | 0.015138 |
| ALMERIA | 71 | 0.014723 |
| Teruel | 70 | 0.014516 |
| Araba/Alava | 70 | 0.014516 |
| CAdiz | 65 | 0.013479 |
| ALAVA | 63 | 0.013064 |
| CACERES | 60 | 0.012442 |
| Melilla | 60 | 0.012442 |
| Tenerife | 59 | 0.012235 |
| Soria | 57 | 0.011820 |
| MAlaga | 55 | 0.011405 |
| Ávila | 48 | 0.009954 |
| TENERIFE | 47 | 0.009747 |
| madrid | 45 | 0.009332 |
| BALEARES | 40 | 0.008295 |
| Valencia/ValEncia | 40 | 0.008295 |
| ISLAS BALEARES | 34 | 0.007051 |
| BILBAO | 33 | 0.006843 |
| avila | 32 | 0.006636 |
| GuipUzcoa | 32 | 0.006636 |
| AVILA | 32 | 0.006636 |
| Ceuta | 31 | 0.006429 |
| Guipuzkoa | 29 | 0.006014 |
| Islas Baleares | 26 | 0.005392 |
| Avila | 24 | 0.004977 |
| MALLORCA | 24 | 0.004977 |
| GRAN CANARIA | 22 | 0.004562 |
| A Coru?a | 21 | 0.004355 |
| LAS PALMAS DE GRAN CANARIA | 21 | 0.004355 |
| CANARIAS | 20 | 0.004147 |
| Bilbao | 19 | 0.003940 |
| LA CORUÑA | 18 | 0.003733 |
| VALENCIA/VALéNCIA | 17 | 0.003525 |
| GERONA | 17 | 0.003525 |
| CORUÑA | 17 | 0.003525 |
| VALENCIA/VALÉNCIA | 16 | 0.003318 |
| VIGO | 15 | 0.003111 |
| barcelona | 14 | 0.002903 |
| Bizcaia | 14 | 0.002903 |
| malaga | 14 | 0.002903 |
| Valencia/Valéncia | 14 | 0.002903 |
| PALMA DE MALLORCA | 13 | 0.002696 |
| Castelló | 13 | 0.002696 |
| valencia | 13 | 0.002696 |
| Guipuzcua | 12 | 0.002488 |
| AlmerIa | 12 | 0.002488 |
| ORENSE | 11 | 0.002281 |
| Las Palmas de Gran Canarias | 11 | 0.002281 |
| PAMPLONA | 11 | 0.002281 |
| CastellOn/CastellO | 10 | 0.002074 |
| sevilla | 10 | 0.002074 |
| IBIZA | 10 | 0.002074 |
| GUIPUZCUA | 10 | 0.002074 |
| alicante | 10 | 0.002074 |
| M?laga | 10 | 0.002074 |
| GUIPUZKOA | 9 | 0.001866 |
| CaDIZ | 9 | 0.001866 |
| OVIEDO | 9 | 0.001866 |
| CAceres | 9 | 0.001866 |
| Araba/Álava | 9 | 0.001866 |
| cadiz | 8 | 0.001659 |
| ALACANT | 8 | 0.001659 |
| MaLAGA | 8 | 0.001659 |
| LERIDA | 8 | 0.001659 |
| ARABA/ALAVA | 8 | 0.001659 |
| COrdoba | 8 | 0.001659 |
| asturias | 7 | 0.001452 |
| GALICIA | 7 | 0.001452 |
| Orense | 7 | 0.001452 |
| GUIPÚZCOA | 7 | 0.001452 |
| murcia | 6 | 0.001244 |
| GIJON | 6 | 0.001244 |
| LAS PALMAS DE GRAN CANARIAS | 6 | 0.001244 |
| salamanca | 6 | 0.001244 |
| MENORCA | 6 | 0.001244 |
| toledo | 6 | 0.001244 |
| Santander | 6 | 0.001244 |
| Araba | 6 | 0.001244 |
| La Coruña | 6 | 0.001244 |
| Vizkaya | 6 | 0.001244 |
| badajoz | 6 | 0.001244 |
| A coruña | 6 | 0.001244 |
| Mallorca | 6 | 0.001244 |
| SANTANDER | 5 | 0.001037 |
| Las Palmas De Gran Canaria | 5 | 0.001037 |
| Gerona | 5 | 0.001037 |
| VIZKAYA | 5 | 0.001037 |
| ANDORRA | 5 | 0.001037 |
| ÁLAVA | 5 | 0.001037 |
| CASTELLÓN | 5 | 0.001037 |
| VIZCAIA | 5 | 0.001037 |
| LANZAROTE | 5 | 0.001037 |
| Gipuzcoa | 5 | 0.001037 |
| Málaga | 5 | 0.001037 |
| Las Palmas de Gran Canaria | 5 | 0.001037 |
| ILLES BALEARES | 5 | 0.001037 |
| SAN SEBASTIAN | 5 | 0.001037 |
| LOGROÑO | 5 | 0.001037 |
| Coruña | 5 | 0.001037 |
| vizcaya | 5 | 0.001037 |
| SANTA CRUZ TENERIFE | 5 | 0.001037 |
| Vigo | 4 | 0.000829 |
| Cartagena | 4 | 0.000829 |
| Oviedo | 4 | 0.000829 |
| Illes Baleares | 4 | 0.000829 |
| Palma De Mallorca | 4 | 0.000829 |
| BArcelonA | 4 | 0.000829 |
| PAIS VASCO | 4 | 0.000829 |
| BIZCAYA | 4 | 0.000829 |
| valladolid | 4 | 0.000829 |
| SevillA | 4 | 0.000829 |
| Guipuzkua | 4 | 0.000829 |
| pontevedra | 4 | 0.000829 |
| AlIcante/Alacant | 4 | 0.000829 |
| MadrId | 4 | 0.000829 |
| Gran Canaria | 4 | 0.000829 |
| santa cruz de tenerife | 4 | 0.000829 |
| A CORU?A | 4 | 0.000829 |
| BIZCAIA | 4 | 0.000829 |
| CARTAGENA | 4 | 0.000829 |
| a coruña | 3 | 0.000622 |
| CáDIZ | 3 | 0.000622 |
| Canarias | 3 | 0.000622 |
| Asturia | 3 | 0.000622 |
| Logroño | 3 | 0.000622 |
| A Coruña | 3 | 0.000622 |
| C?diz | 3 | 0.000622 |
| ARABA | 3 | 0.000622 |
| segovia | 3 | 0.000622 |
| VITORIA | 3 | 0.000622 |
| MAdrid | 3 | 0.000622 |
| MurcIa | 3 | 0.000622 |
| Guipúzcoa | 3 | 0.000622 |
| ?lava | 3 | 0.000622 |
| Pamplona | 3 | 0.000622 |
| DONOSTIA | 3 | 0.000622 |
| cantabria | 3 | 0.000622 |
| LA PALMA | 3 | 0.000622 |
| CORUÑA, A | 3 | 0.000622 |
| CASTELLON/CASTELLO | 3 | 0.000622 |
| SANTIAGO DE COMPOSTELA | 3 | 0.000622 |
| Lanzarote | 3 | 0.000622 |
| ISLAS CANARIAS | 3 | 0.000622 |
| LeOn | 3 | 0.000622 |
| GUIPUZKUA | 3 | 0.000622 |
| Albecete | 3 | 0.000622 |
| burgos | 3 | 0.000622 |
| STA. CRUZ DE TENERIFE | 3 | 0.000622 |
| CASTELLoN/CASTELLo | 3 | 0.000622 |
| cordoba | 3 | 0.000622 |
| Santa Cruz Tenerife | 3 | 0.000622 |
| Bizcaya | 3 | 0.000622 |
| C?ceres | 3 | 0.000622 |
| Guip?zcoa | 3 | 0.000622 |
| JaEn | 3 | 0.000622 |
| Castell?n | 3 | 0.000622 |
| C?rdoba | 3 | 0.000622 |
| Alacant / Alicante | 3 | 0.000622 |
| DONOSTI | 3 | 0.000622 |
| Valladolidad | 2 | 0.000415 |
| Hessen | 2 | 0.000415 |
| Vitoria | 2 | 0.000415 |
| tenerife | 2 | 0.000415 |
| almeria | 2 | 0.000415 |
| bizkaia | 2 | 0.000415 |
| Santa cruz de Tenerife | 2 | 0.000415 |
| POTEVEDRA | 2 | 0.000415 |
| Asturies | 2 | 0.000415 |
| LA PALMAS DE GRAN CANARIA | 2 | 0.000415 |
| ZAGAROZA | 2 | 0.000415 |
| jaen | 2 | 0.000415 |
| Portugal | 2 | 0.000415 |
| EXTREMADURA | 2 | 0.000415 |
| CORU?A | 2 | 0.000415 |
| Cádiz | 2 | 0.000415 |
| Zaragoz | 2 | 0.000415 |
| Malága | 2 | 0.000415 |
| Alicante/alacant | 2 | 0.000415 |
| Vizkaia | 2 | 0.000415 |
| LEoN | 2 | 0.000415 |
| CASTELLÓ | 2 | 0.000415 |
| FRANCIA | 2 | 0.000415 |
| Gijón | 2 | 0.000415 |
| Marbella | 2 | 0.000415 |
| SAN CRUZ DE TENERIFE | 2 | 0.000415 |
| MARBELLA | 2 | 0.000415 |
| MáLAGA | 2 | 0.000415 |
| CORUÑA,A | 2 | 0.000415 |
| CIUDAD | 2 | 0.000415 |
| EXTRANJERO | 2 | 0.000415 |
| ALEMANIA | 2 | 0.000415 |
| illes balears | 2 | 0.000415 |
| zaragoza | 2 | 0.000415 |
| GuIpuzcoa | 2 | 0.000415 |
| Valencia/valència | 2 | 0.000415 |
| SALAMNCA | 2 | 0.000415 |
| girona | 2 | 0.000415 |
| AlicAnte/AlAcAnt | 2 | 0.000415 |
| JAeN | 2 | 0.000415 |
| ACORUÑA | 2 | 0.000415 |
| CASTILLA Y LEON | 2 | 0.000415 |
| Almer?a | 2 | 0.000415 |
| Zaragona | 2 | 0.000415 |
| tarragona | 2 | 0.000415 |
| ASTURIA | 2 | 0.000415 |
| TARRRAGONA | 2 | 0.000415 |
| LLeida | 2 | 0.000415 |
| caceres | 2 | 0.000415 |
| Le?n | 2 | 0.000415 |
| lugo | 2 | 0.000415 |
| Islas Canarias | 2 | 0.000415 |
| zamora | 2 | 0.000415 |
| CANARIA | 2 | 0.000415 |
| ValencIa/ValencIa | 2 | 0.000415 |
| NAVARA | 1 | 0.000207 |
| palma de mallorca | 1 | 0.000207 |
| Gipizkoa | 1 | 0.000207 |
| Mállaga | 1 | 0.000207 |
| BIzkaia | 1 | 0.000207 |
| PONTEVDRA | 1 | 0.000207 |
| Gipuzloa | 1 | 0.000207 |
| Gipuzkua | 1 | 0.000207 |
| palencia | 1 | 0.000207 |
| balears | 1 | 0.000207 |
| MurciA | 1 | 0.000207 |
| Guizpuzcoa | 1 | 0.000207 |
| GORLIZ | 1 | 0.000207 |
| LA CORU?A | 1 | 0.000207 |
| Getxo/Bizkaia | 1 | 0.000207 |
| Illes Balers | 1 | 0.000207 |
| VizcAyA | 1 | 0.000207 |
| AvilA | 1 | 0.000207 |
| gijón | 1 | 0.000207 |
| VITORIA-GASTEIZ | 1 | 0.000207 |
| PAMPLOANA | 1 | 0.000207 |
| GELVES | 1 | 0.000207 |
| Islas Balears | 1 | 0.000207 |
| la coruña | 1 | 0.000207 |
| VAlenciA/VAlenciA | 1 | 0.000207 |
| MelillA | 1 | 0.000207 |
| LLIEDA | 1 | 0.000207 |
| MALGA | 1 | 0.000207 |
| Illes De Balears | 1 | 0.000207 |
| CaCERES | 1 | 0.000207 |
| Águilas | 1 | 0.000207 |
| Illes Ballears | 1 | 0.000207 |
| navarra | 1 | 0.000207 |
| Santiago | 1 | 0.000207 |
| VALENCIA/VALENCIA | 1 | 0.000207 |
| CORUÑA A | 1 | 0.000207 |
| granada | 1 | 0.000207 |
| Guadalaja | 1 | 0.000207 |
| Gipozkoa | 1 | 0.000207 |
| Alemania | 1 | 0.000207 |
| Araba/álava | 1 | 0.000207 |
| guipuzkoa | 1 | 0.000207 |
| Donosti | 1 | 0.000207 |
| Extremadura | 1 | 0.000207 |
| bilbao | 1 | 0.000207 |
| Peñiscola | 1 | 0.000207 |
| SANTA CRUZ DETENERIFE | 1 | 0.000207 |
| SEVIILA | 1 | 0.000207 |
| BIZKAIYA | 1 | 0.000207 |
| Roma | 1 | 0.000207 |
| Pontevdra | 1 | 0.000207 |
| Servilla | 1 | 0.000207 |
| PALMA MALLORCA | 1 | 0.000207 |
| Araba/alava | 1 | 0.000207 |
| GUIPOUZCOA | 1 | 0.000207 |
| A CoruñA | 1 | 0.000207 |
| OTUR VALDES (LUARCA) | 1 | 0.000207 |
| GRANADILLA DE ABONA | 1 | 0.000207 |
| GIPUSCUA | 1 | 0.000207 |
| santarder | 1 | 0.000207 |
| Seilla | 1 | 0.000207 |
| ciudad real | 1 | 0.000207 |
| GUIPUCOA | 1 | 0.000207 |
| LORCA | 1 | 0.000207 |
| PALMAS DE GRAN CANARIAS | 1 | 0.000207 |
| Montcada I Reixac | 1 | 0.000207 |
| guipuzcoa | 1 | 0.000207 |
| rARRAGONA | 1 | 0.000207 |
| Illes Belears | 1 | 0.000207 |
| Bsarcelona | 1 | 0.000207 |
| AUSTURIAS | 1 | 0.000207 |
| Vizacaya | 1 | 0.000207 |
| guipzkoa | 1 | 0.000207 |
| BADALONA | 1 | 0.000207 |
| APOLA | 1 | 0.000207 |
| S/C DE TENERIFE | 1 | 0.000207 |
| TARRAGON | 1 | 0.000207 |
| Gudalajara | 1 | 0.000207 |
| A CORUA | 1 | 0.000207 |
| Castello | 1 | 0.000207 |
| Tarrragona | 1 | 0.000207 |
| BIZKAYA | 1 | 0.000207 |
| BALERAES | 1 | 0.000207 |
| Castellón/Castello | 1 | 0.000207 |
| DE JAEN | 1 | 0.000207 |
| VIzcaya | 1 | 0.000207 |
| Guipuzcuoa | 1 | 0.000207 |
| Gipuzkia | 1 | 0.000207 |
| Valdegovía | 1 | 0.000207 |
| ciudda real | 1 | 0.000207 |
| PONTEVEDRO | 1 | 0.000207 |
| Pontebra | 1 | 0.000207 |
| Guipuscoa | 1 | 0.000207 |
| Bizkaya | 1 | 0.000207 |
| Las Palamas | 1 | 0.000207 |
| Araba/Álaba | 1 | 0.000207 |
| SALMANCA | 1 | 0.000207 |
| Barcleona | 1 | 0.000207 |
| Cantábria | 1 | 0.000207 |
| BALEARS | 1 | 0.000207 |
| Pontevendra | 1 | 0.000207 |
| Taragona | 1 | 0.000207 |
| VIZCAYIA | 1 | 0.000207 |
| Alicante (Alacant) | 1 | 0.000207 |
| VALLADOLD | 1 | 0.000207 |
| Tarrronga | 1 | 0.000207 |
| CASTILLA | 1 | 0.000207 |
| ARONA | 1 | 0.000207 |
| Alicante/Alacantt | 1 | 0.000207 |
| SANTA CRUZ DE TRENERIFE | 1 | 0.000207 |
| a Coruña | 1 | 0.000207 |
| BURJASOL | 1 | 0.000207 |
| vigo | 1 | 0.000207 |
| GUIPUZ | 1 | 0.000207 |
| Las Palma | 1 | 0.000207 |
| ZARAGONA | 1 | 0.000207 |
| BAEARES | 1 | 0.000207 |
| BIZAKAIA | 1 | 0.000207 |
| BENALMADENA | 1 | 0.000207 |
| CASTELLoN | 1 | 0.000207 |
| ALGUAZAS | 1 | 0.000207 |
| Garnada | 1 | 0.000207 |
| CORBOBA | 1 | 0.000207 |
| PO | 1 | 0.000207 |
| Vallodolid | 1 | 0.000207 |
| ILLESBALEARS | 1 | 0.000207 |
| A CORUÑA | 1 | 0.000207 |
| TERRAGONA | 1 | 0.000207 |
| Ja?n | 1 | 0.000207 |
| Guipizcoa | 1 | 0.000207 |
| Arava/Álava | 1 | 0.000207 |
| Zarago | 1 | 0.000207 |
| Santa Cruz De Tenerfie | 1 | 0.000207 |
| MIERES | 1 | 0.000207 |
| araba | 1 | 0.000207 |
| LA RIJOA | 1 | 0.000207 |
| GUPUZCOA | 1 | 0.000207 |
| Ãlava | 1 | 0.000207 |
| ALBECETE | 1 | 0.000207 |
| CORU?A,A | 1 | 0.000207 |
| SANTA CRUZ DE TENERIFA | 1 | 0.000207 |
| SAN SESBAST | 1 | 0.000207 |
| VICTORIA | 1 | 0.000207 |
| ILLES | 1 | 0.000207 |
| VIZAYA | 1 | 0.000207 |
| albacete | 1 | 0.000207 |
| Todelo | 1 | 0.000207 |
| PEILAGOS | 1 | 0.000207 |
| PICAXEN | 1 | 0.000207 |
| Guipozcoa | 1 | 0.000207 |
| PONTVEDRRDA | 1 | 0.000207 |
| ARABA/aLAVA | 1 | 0.000207 |
| BALEARES, ISLAS | 1 | 0.000207 |
| LAs PAlmAs | 1 | 0.000207 |
| STA LUCIA TIRAJANAGRAN CANARIA | 1 | 0.000207 |
| SEVILA | 1 | 0.000207 |
| Coto de Bornos | 1 | 0.000207 |
| VIZKAIA | 1 | 0.000207 |
| PONTEVENDRA | 1 | 0.000207 |
| STA DE CRUZ DE TENERIFE | 1 | 0.000207 |
| VALLLADOLID | 1 | 0.000207 |
| VIZCAA | 1 | 0.000207 |
| VALLADOLIS | 1 | 0.000207 |
| VALLADALID | 1 | 0.000207 |
| VALENIA | 1 | 0.000207 |
| GUIPOCUA | 1 | 0.000207 |
| PASCO VASCO | 1 | 0.000207 |
| PALMA | 1 | 0.000207 |
| Mayorca | 1 | 0.000207 |
| EVILLA | 1 | 0.000207 |
| Cádiaz | 1 | 0.000207 |
| DENIA | 1 | 0.000207 |
| LA ALBERCA | 1 | 0.000207 |
| a coruñpa | 1 | 0.000207 |
| Bizckai | 1 | 0.000207 |
| CANTAMBRIA | 1 | 0.000207 |
| Valencia/Val?ncia | 1 | 0.000207 |
| VIZACAYA | 1 | 0.000207 |
| TARAGONA | 1 | 0.000207 |
| AlmerÃa | 1 | 0.000207 |
| Castellón/Castelló | 1 | 0.000207 |
| MOTRIL | 1 | 0.000207 |
| guipuzcua | 1 | 0.000207 |
| MUERCIA | 1 | 0.000207 |
| POLA DE LENA-ASTURIAS | 1 | 0.000207 |
| Francia | 1 | 0.000207 |
| CATALUÑA | 1 | 0.000207 |
| AlIcante | 1 | 0.000207 |
| VALLODOLID | 1 | 0.000207 |
| SEVILLLA | 1 | 0.000207 |
| ZARAUTZ | 1 | 0.000207 |
| GRANADAS | 1 | 0.000207 |
| CASTILLA DE LEON | 1 | 0.000207 |
| GIPUZCUA | 1 | 0.000207 |
| GUALAJARA | 1 | 0.000207 |
| GUIPUZOCA | 1 | 0.000207 |
| GuIpUzcoa | 1 | 0.000207 |
| ANDALUCIA | 1 | 0.000207 |
| VIZCVAYA | 1 | 0.000207 |
| VITORIA GASTEIZ | 1 | 0.000207 |
| ISLAS BALERES | 1 | 0.000207 |
| GUIPUIZCOA | 1 | 0.000207 |
| CASTELLON DE LA PLANA | 1 | 0.000207 |
| FUERTEVENTURA | 1 | 0.000207 |
| Vizvaya | 1 | 0.000207 |
| Valencai | 1 | 0.000207 |
| Arona | 1 | 0.000207 |
| Murica | 1 | 0.000207 |
| Lerida | 1 | 0.000207 |
| Viscaya | 1 | 0.000207 |
| Las Palmas De Gran Canarias | 1 | 0.000207 |
| Valldolid | 1 | 0.000207 |
| Vlencia | 1 | 0.000207 |
| Andorra | 1 | 0.000207 |
| MALPICA DE BERGANTIÑOS | 1 | 0.000207 |
| badajod | 1 | 0.000207 |
| Alicate | 1 | 0.000207 |
| CastellOn | 1 | 0.000207 |
| Fontanarejo | 1 | 0.000207 |
| Mälaga | 1 | 0.000207 |
| SANTA EULALIA DEL RIO | 1 | 0.000207 |
| ZAROGAZA | 1 | 0.000207 |
| Matarrubia | 1 | 0.000207 |
| Turias | 1 | 0.000207 |
| Valéncia | 1 | 0.000207 |
| LA CARUÑA | 1 | 0.000207 |
| BAJADOZ | 1 | 0.000207 |
| LAS PALMAS (LANZAROTE) | 1 | 0.000207 |
| PATERNA | 1 | 0.000207 |
| GUIPOZKOA | 1 | 0.000207 |
| FELANITX | 1 | 0.000207 |
| CIudad Real | 1 | 0.000207 |
| LleIda | 1 | 0.000207 |
| La Coru?a | 1 | 0.000207 |
| SevIlla | 1 | 0.000207 |
| SAN SE BASTIAN | 1 | 0.000207 |
| PONTEVERA | 1 | 0.000207 |
| CALLELLON | 1 | 0.000207 |
| BRION | 1 | 0.000207 |
| baleares | 1 | 0.000207 |
| CUDAD REAL | 1 | 0.000207 |
| STA CRUZ DE TERENIFE | 1 | 0.000207 |
| GIPUZCOA | 1 | 0.000207 |
| BEJAR | 1 | 0.000207 |
| GUIPOZCOA | 1 | 0.000207 |
| La Pama | 1 | 0.000207 |
| FRONSAC | 1 | 0.000207 |
| GRANDA | 1 | 0.000207 |
| A Coruna | 1 | 0.000207 |
| AQUITANIA | 1 | 0.000207 |
| La rioja | 1 | 0.000207 |
| ILLES BALEARS MENORCA | 1 | 0.000207 |
| aLAVA | 1 | 0.000207 |
| Castilla y León | 1 | 0.000207 |
| ALABA | 1 | 0.000207 |
| ALMERiA | 1 | 0.000207 |
| BARCELONAc23090 | 1 | 0.000207 |
| Alicane | 1 | 0.000207 |
| Alicante/Alcant | 1 | 0.000207 |
| Castellóna | 1 | 0.000207 |
| gRANADA | 1 | 0.000207 |
| PORTUGAL | 1 | 0.000207 |
| FERROL | 1 | 0.000207 |
| Toledo. | 1 | 0.000207 |
| CASTELLO | 1 | 0.000207 |
| Valrencia | 1 | 0.000207 |
| Algeciras | 1 | 0.000207 |
| TARRAGORRA | 1 | 0.000207 |
| Palma de Mallorca | 1 | 0.000207 |
| PARIS | 1 | 0.000207 |
| SUIZA | 1 | 0.000207 |
| Tarrgona | 1 | 0.000207 |
| lanzarote | 1 | 0.000207 |
| Sta Cruz De Tenerife | 1 | 0.000207 |
| sant sadurni de noia | 1 | 0.000207 |
| San Sebastin | 1 | 0.000207 |
| MAlAgA | 1 | 0.000207 |
| Albacte | 1 | 0.000207 |
| Cieza | 1 | 0.000207 |
| SALALANCA | 1 | 0.000207 |
| ALVA | 1 | 0.000207 |
| Castellón de la Plana | 1 | 0.000207 |
| Pontvendra | 1 | 0.000207 |
| Santa Cruz de Tenerifie | 1 | 0.000207 |
| Rioja,la | 1 | 0.000207 |
| XATIVA | 1 | 0.000207 |
| Luego | 1 | 0.000207 |
| Badiajoz | 1 | 0.000207 |
| S.C. TENERIFE | 1 | 0.000207 |
| Fuengirola | 1 | 0.000207 |
| Munchen | 1 | 0.000207 |
| CoRDOBA | 1 | 0.000207 |
| SANTA CRUZ DE La PALMA | 1 | 0.000207 |
| Paris | 1 | 0.000207 |
| VICTORAI GAXTEIZ | 1 | 0.000207 |
| LE0N | 1 | 0.000207 |
| Catarroja | 1 | 0.000207 |
| Albate | 1 | 0.000207 |
| Navara | 1 | 0.000207 |
| Las Palmas - Telde | 1 | 0.000207 |
| LAS PALMAS DE GRAN CANARIOS | 1 | 0.000207 |
| Schwieberdingen | 1 | 0.000207 |
| Aragón | 1 | 0.000207 |
| Alicante. | 1 | 0.000207 |
| mallorca | 1 | 0.000207 |
| TARRAGAONA | 1 | 0.000207 |
| A | 1 | 0.000207 |
| PALMA DE MALORCA | 1 | 0.000207 |
| LAS PALMAS GRAN CANARIAS | 1 | 0.000207 |
| LANZARATE | 1 | 0.000207 |
| islas Baleares | 1 | 0.000207 |
| ESPAÑA | 1 | 0.000207 |
| YEIDA | 1 | 0.000207 |
| Astudias | 1 | 0.000207 |
| RIOJA,LA | 1 | 0.000207 |
| GRAN CANARIAS | 1 | 0.000207 |
| Castellon/Castelló | 1 | 0.000207 |
| Barelona | 1 | 0.000207 |
| LAS PALMAS GRAN CANARIA | 1 | 0.000207 |
| Sta.cruz Tenerife | 1 | 0.000207 |
| bizcaia | 1 | 0.000207 |
| VILLAPEDRE | 1 | 0.000207 |
| VAL DE MARNE | 1 | 0.000207 |
| BABIERA | 1 | 0.000207 |
| GRAN CANARIAS - LAS PALMAS | 1 | 0.000207 |
| A CORUNA | 1 | 0.000207 |
| ORENZE | 1 | 0.000207 |
| Cáceres | 1 | 0.000207 |
| Corboda | 1 | 0.000207 |
| Cordoba Ibarruri 3 esc 1 3 1 | 1 | 0.000207 |
| Sant vicente | 1 | 0.000207 |
| Balears | 1 | 0.000207 |
| Castilla y la Mancha | 1 | 0.000207 |
| Las Baleares | 1 | 0.000207 |
| Fuerteventura | 1 | 0.000207 |
| Guipuzkuo | 1 | 0.000207 |
| Gupuzcoa | 1 | 0.000207 |
| ARRASATE/MONDRAGON | 1 | 0.000207 |
| Elche | 1 | 0.000207 |
| gipuzkoa | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npsp__largest_soft_credit_amount__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npsp__largest_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_last_year__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__soft_credit_last_year__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_last_year__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_this_year__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__soft_credit_this_year__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_this_year__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_two_years_ago__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__soft_credit_two_years_ago__c es 482224. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_two_years_ago__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondoscp__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 406361 | 84.268099 |
| True | 75863 | 15.731901 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosemail__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 442371 | 91.735583 |
| True | 39853 | 8.264417 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosmi__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 449504 | 93.214772 |
| True | 32720 | 6.785228 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondossms__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 448257 | 92.956178 |
| True | 33967 | 7.043822 |
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaignentryrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstcampaignentryrecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaignentryrecurringdonor__c es 1. Lo que supone un 0.0002073725073824613%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mrBSQAY | 16831 | 3.490287 |
| 7013Y000001mr2cQAA | 14005 | 2.904252 |
| 7013Y000001mr2DQAQ | 12762 | 2.646488 |
| 7013Y000001mr1MQAQ | 12257 | 2.541765 |
| 7013Y000001mrCzQAI | 11836 | 2.454461 |
| ... | ... | ... |
| 7013Y000001vXGOQA2 | 1 | 0.000207 |
| 7013Y000001mrE4QAI | 1 | 0.000207 |
| 7013Y000001mrAIQAY | 1 | 0.000207 |
| 7013Y000001mr7OQAQ | 1 | 0.000207 |
| 7013Y000001mrY3QAI | 1 | 0.000207 |
2288 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaingcolaboration__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstcampaingcolaboration__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaingcolaboration__c es 714. Lo que supone un 0.14806397027107734%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mrCzQAI | 19195 | 3.980515 |
| 7013Y000001mrBSQAY | 16604 | 3.443213 |
| 7013Y000001mr2cQAA | 13224 | 2.742294 |
| 7013Y000001mr1MQAQ | 12160 | 2.521650 |
| 7013Y000001mr2DQAQ | 11853 | 2.457986 |
| ... | ... | ... |
| 7013Y000001mrAMQAY | 1 | 0.000207 |
| 7013Y000001mrTEQAY | 1 | 0.000207 |
| 7013Y000001mrGQQAY | 1 | 0.000207 |
| 7013Y000001mr0BQAQ | 1 | 0.000207 |
| 7013Y000001mrY3QAI | 1 | 0.000207 |
2465 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_firstannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_firstannualizedquota__c es 1. Lo que supone un 0.0002073725073824613% El nº de vacios para la variable msf_firstannualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 140983 | 29.236059 |
| 6.000000e+01 | 58439 | 12.118667 |
| 1.800000e+02 | 52974 | 10.985374 |
| 2.400000e+02 | 26696 | 5.536028 |
| 7.200000e+01 | 25395 | 5.266236 |
| 1.440000e+02 | 21497 | 4.457896 |
| 7.212000e+01 | 16501 | 3.421861 |
| 3.600000e+01 | 10931 | 2.266794 |
| 3.600000e+02 | 9672 | 2.005711 |
| 9.600000e+01 | 8647 | 1.793154 |
| 1.000000e+02 | 8576 | 1.778430 |
| 3.000000e+02 | 7661 | 1.588684 |
| 5.000000e+01 | 7245 | 1.502417 |
| 5.196000e+01 | 6568 | 1.362025 |
| 6.010000e+01 | 4736 | 0.982118 |
| 4.000000e+01 | 4464 | 0.925713 |
| 3.000000e+01 | 4171 | 0.864953 |
| 8.000000e+01 | 4166 | 0.863916 |
| 2.000000e+01 | 4031 | 0.835920 |
| 3.005000e+01 | 3790 | 0.785943 |
| 8.400000e+01 | 3763 | 0.780344 |
| 1.202000e+02 | 3429 | 0.711082 |
| 2.000000e+02 | 3071 | 0.636842 |
| 1.442400e+02 | 2644 | 0.548294 |
| 4.800000e+01 | 2281 | 0.473018 |
| 1.500000e+02 | 2248 | 0.466174 |
| 2.163600e+02 | 2238 | 0.464101 |
| 6.000000e+02 | 2204 | 0.457050 |
| 1.000000e+01 | 1902 | 0.394423 |
| 1.200000e+01 | 1885 | 0.390898 |
| 3.606000e+02 | 1773 | 0.367672 |
| 1.320000e+02 | 1502 | 0.311474 |
| 1.803000e+01 | 1410 | 0.292396 |
| 1.500000e+01 | 1341 | 0.278087 |
| 2.160000e+02 | 1183 | 0.245322 |
| 9.015000e+01 | 1045 | 0.216705 |
| 7.200000e+02 | 1021 | 0.211728 |
| 2.500000e+01 | 995 | 0.206336 |
| 2.404000e+02 | 868 | 0.180000 |
| 1.080000e+02 | 858 | 0.177926 |
| 9.000000e+01 | 770 | 0.159677 |
| 4.800000e+02 | 747 | 0.154908 |
| 4.808000e+01 | 668 | 0.138525 |
| 1.600000e+02 | 630 | 0.130645 |
| 1.200000e+03 | 557 | 0.115507 |
| 3.486000e+01 | 547 | 0.113433 |
| 2.400000e+01 | 517 | 0.107212 |
| 4.000000e+02 | 491 | 0.101820 |
| 2.404000e+01 | 479 | 0.099332 |
| 1.560000e+02 | 441 | 0.091451 |
| 2.040000e+02 | 441 | 0.091451 |
| 1.502500e+02 | 429 | 0.088963 |
| 3.606000e+01 | 387 | 0.080253 |
| 1.394400e+02 | 379 | 0.078594 |
| 1.040400e+02 | 370 | 0.076728 |
| 1.920000e+02 | 359 | 0.074447 |
| 7.000000e+01 | 346 | 0.071751 |
| 7.212000e+02 | 336 | 0.069677 |
| 7.500000e+01 | 313 | 0.064908 |
| 1.082400e+02 | 300 | 0.062212 |
| 3.612000e+01 | 269 | 0.055783 |
| 2.500000e+02 | 264 | 0.054746 |
| 1.803600e+02 | 237 | 0.049147 |
| 1.680000e+02 | 226 | 0.046866 |
| 4.200000e+02 | 204 | 0.042304 |
| 5.000000e+02 | 198 | 0.041060 |
| 9.316000e+01 | 187 | 0.038779 |
| 1.039200e+02 | 175 | 0.036290 |
| 6.010000e+00 | 165 | 0.034217 |
| 1.400000e+02 | 162 | 0.033594 |
| 9.616000e+01 | 152 | 0.031521 |
| 2.520000e+02 | 148 | 0.030691 |
| 1.803000e+02 | 146 | 0.030276 |
| 1.202000e+01 | 143 | 0.029654 |
| 2.640000e+02 | 140 | 0.029032 |
| 2.884800e+02 | 131 | 0.027166 |
| 2.880000e+02 | 128 | 0.026544 |
| 5.000000e+00 | 122 | 0.025299 |
| 5.200000e+01 | 122 | 0.025299 |
| 1.730400e+02 | 121 | 0.025092 |
| 3.608000e+01 | 118 | 0.024470 |
| 7.224000e+01 | 117 | 0.024263 |
| 3.200000e+01 | 105 | 0.021774 |
| 5.400000e+02 | 99 | 0.020530 |
| 1.000000e+03 | 95 | 0.019700 |
| 3.005100e+02 | 94 | 0.019493 |
| 4.183200e+02 | 90 | 0.018664 |
| 5.768000e+01 | 89 | 0.018456 |
| 1.800000e+01 | 89 | 0.018456 |
| 3.500000e+01 | 85 | 0.017627 |
| 3.000000e+00 | 79 | 0.016382 |
| 1.250000e+02 | 78 | 0.016175 |
| 6.012000e+01 | 72 | 0.014931 |
| 4.207000e+01 | 71 | 0.014723 |
| 1.800000e+03 | 71 | 0.014723 |
| 4.500000e+01 | 67 | 0.013894 |
| 4.320000e+02 | 66 | 0.013687 |
| 2.885000e+01 | 62 | 0.012857 |
| 6.000000e+00 | 60 | 0.012442 |
| 1.923200e+02 | 54 | 0.011198 |
| 8.414000e+01 | 50 | 0.010369 |
| 1.300000e+02 | 48 | 0.009954 |
| 1.081800e+03 | 45 | 0.009332 |
| 8.000000e+00 | 44 | 0.009124 |
| 1.080000e+03 | 43 | 0.008917 |
| 5.409000e+01 | 41 | 0.008502 |
| 1.442000e+01 | 41 | 0.008502 |
| 0.000000e+00 | 40 | 0.008295 |
| 9.600000e+02 | 40 | 0.008295 |
| 3.120000e+02 | 40 | 0.008295 |
| 2.400000e+03 | 40 | 0.008295 |
| 4.327200e+02 | 39 | 0.008088 |
| 6.010000e+02 | 39 | 0.008088 |
| 5.500000e+01 | 38 | 0.007880 |
| 8.000000e+02 | 38 | 0.007880 |
| 1.154000e+02 | 37 | 0.007673 |
| 1.100000e+02 | 37 | 0.007673 |
| 1.500000e+03 | 35 | 0.007258 |
| 4.200000e+01 | 35 | 0.007258 |
| 4.808000e+02 | 34 | 0.007051 |
| 5.770000e+01 | 34 | 0.007051 |
| 9.000000e+02 | 34 | 0.007051 |
| 8.400000e+02 | 34 | 0.007051 |
| 3.462000e+02 | 33 | 0.006843 |
| 6.010100e+02 | 32 | 0.006636 |
| 1.440000e+03 | 32 | 0.006636 |
| 3.960000e+02 | 30 | 0.006221 |
| 2.760000e+02 | 30 | 0.006221 |
| 1.081800e+02 | 30 | 0.006221 |
| 3.726400e+02 | 28 | 0.005806 |
| 3.200000e+02 | 27 | 0.005599 |
| 3.600000e+03 | 26 | 0.005392 |
| 1.682800e+02 | 25 | 0.005184 |
| 1.040000e+02 | 25 | 0.005184 |
| 2.404100e+02 | 24 | 0.004977 |
| 3.614400e+02 | 23 | 0.004770 |
| 1.803200e+02 | 23 | 0.004770 |
| 2.524800e+02 | 23 | 0.004770 |
| 3.500000e+02 | 23 | 0.004770 |
| 3.000000e+03 | 22 | 0.004562 |
| 5.769600e+02 | 21 | 0.004355 |
| 6.500000e+01 | 21 | 0.004355 |
| 2.800000e+02 | 21 | 0.004355 |
| 5.048400e+02 | 20 | 0.004147 |
| 2.200000e+02 | 20 | 0.004147 |
| 3.840000e+02 | 19 | 0.003940 |
| 3.240000e+02 | 19 | 0.003940 |
| 3.650000e+02 | 18 | 0.003733 |
| 2.280000e+02 | 17 | 0.003525 |
| 1.204800e+02 | 17 | 0.003525 |
| 1.700000e+02 | 17 | 0.003525 |
| 2.000000e+03 | 17 | 0.003525 |
| 8.800000e+01 | 17 | 0.003525 |
| 1.082000e+02 | 17 | 0.003525 |
| 5.400000e+01 | 17 | 0.003525 |
| 2.800000e+01 | 17 | 0.003525 |
| 8.654400e+02 | 16 | 0.003318 |
| 1.204000e+01 | 16 | 0.003318 |
| 1.600000e+01 | 16 | 0.003318 |
| 5.040000e+02 | 16 | 0.003318 |
| 6.000000e+03 | 15 | 0.003111 |
| 9.020000e+00 | 15 | 0.003111 |
| 1.442400e+03 | 15 | 0.003111 |
| 2.600000e+02 | 14 | 0.002903 |
| 1.094400e+02 | 14 | 0.002903 |
| 3.360000e+02 | 14 | 0.002903 |
| 6.024000e+01 | 14 | 0.002903 |
| 5.600000e+01 | 14 | 0.002903 |
| 8.416000e+01 | 14 | 0.002903 |
| 3.606100e+02 | 13 | 0.002696 |
| 6.600000e+01 | 13 | 0.002696 |
| 9.200000e+01 | 13 | 0.002696 |
| 7.813000e+01 | 12 | 0.002488 |
| 8.500000e+01 | 11 | 0.002281 |
| 3.720000e+02 | 11 | 0.002281 |
| 4.080000e+02 | 11 | 0.002281 |
| 1.520000e+02 | 11 | 0.002281 |
| 8.460000e+01 | 11 | 0.002281 |
| 3.800000e+01 | 11 | 0.002281 |
| 6.120000e+02 | 10 | 0.002074 |
| 1.480000e+02 | 10 | 0.002074 |
| 2.103500e+02 | 9 | 0.001866 |
| 6.600000e+02 | 9 | 0.001866 |
| 7.800000e+01 | 9 | 0.001866 |
| 3.900000e+01 | 9 | 0.001866 |
| 1.750000e+02 | 9 | 0.001866 |
| 3.480000e+02 | 8 | 0.001659 |
| 1.824000e+02 | 8 | 0.001659 |
| 4.500000e+02 | 8 | 0.001659 |
| 1.120000e+02 | 8 | 0.001659 |
| 1.400000e+01 | 8 | 0.001659 |
| 4.680000e+02 | 8 | 0.001659 |
| 2.308000e+02 | 8 | 0.001659 |
| 8.640000e+02 | 8 | 0.001659 |
| 1.450000e+02 | 7 | 0.001452 |
| 3.012000e+01 | 7 | 0.001452 |
| 6.400000e+01 | 7 | 0.001452 |
| 8.652000e+01 | 7 | 0.001452 |
| 2.160000e+03 | 7 | 0.001452 |
| 7.000000e+00 | 7 | 0.001452 |
| 6.240000e+02 | 7 | 0.001452 |
| 7.600000e+01 | 7 | 0.001452 |
| 1.020000e+02 | 7 | 0.001452 |
| 2.200000e+01 | 7 | 0.001452 |
| 3.400000e+01 | 6 | 0.001244 |
| 4.508000e+01 | 6 | 0.001244 |
| 7.228800e+02 | 6 | 0.001244 |
| 6.800000e+01 | 6 | 0.001244 |
| 1.503000e+01 | 6 | 0.001244 |
| 4.400000e+01 | 6 | 0.001244 |
| 4.332000e+01 | 6 | 0.001244 |
| 6.200000e+01 | 6 | 0.001244 |
| 2.160000e+01 | 6 | 0.001244 |
| 9.036000e+01 | 6 | 0.001244 |
| 1.280000e+02 | 6 | 0.001244 |
| 3.010000e+00 | 6 | 0.001244 |
| 1.020000e+03 | 5 | 0.001037 |
| 2.250000e+02 | 5 | 0.001037 |
| 7.920000e+02 | 5 | 0.001037 |
| 3.005200e+02 | 5 | 0.001037 |
| 1.803000e+03 | 5 | 0.001037 |
| 1.350000e+02 | 5 | 0.001037 |
| 1.200000e+04 | 5 | 0.001037 |
| 5.289000e+01 | 5 | 0.001037 |
| 9.016000e+01 | 5 | 0.001037 |
| 7.400000e+01 | 5 | 0.001037 |
| 1.050000e+02 | 5 | 0.001037 |
| 5.200000e+02 | 5 | 0.001037 |
| 2.600000e+01 | 5 | 0.001037 |
| 1.719600e+02 | 5 | 0.001037 |
| 2.404040e+03 | 4 | 0.000829 |
| 1.600000e+03 | 4 | 0.000829 |
| 6.924000e+02 | 4 | 0.000829 |
| 7.000000e+02 | 4 | 0.000829 |
| 5.592000e+01 | 4 | 0.000829 |
| 7.300000e+01 | 4 | 0.000829 |
| 4.800000e+03 | 4 | 0.000829 |
| 8.660000e+00 | 4 | 0.000829 |
| 9.012000e+01 | 4 | 0.000829 |
| 7.210000e+00 | 4 | 0.000829 |
| 1.201200e+02 | 4 | 0.000829 |
| 1.360000e+02 | 4 | 0.000829 |
| 1.300000e+01 | 4 | 0.000829 |
| 9.000000e+00 | 4 | 0.000829 |
| 7.932000e+01 | 4 | 0.000829 |
| 1.202040e+03 | 4 | 0.000829 |
| 7.800000e+02 | 4 | 0.000829 |
| 7.200000e+03 | 4 | 0.000829 |
| 1.160000e+02 | 4 | 0.000829 |
| 2.100000e+02 | 4 | 0.000829 |
| 1.532600e+02 | 3 | 0.000622 |
| 2.700000e+01 | 3 | 0.000622 |
| 5.000000e+03 | 3 | 0.000622 |
| 2.115600e+02 | 3 | 0.000622 |
| 1.117920e+03 | 3 | 0.000622 |
| 4.000000e+00 | 3 | 0.000622 |
| 5.100000e+01 | 3 | 0.000622 |
| 9.375600e+02 | 3 | 0.000622 |
| 3.606120e+03 | 3 | 0.000622 |
| 1.502400e+02 | 3 | 0.000622 |
| 3.005000e+02 | 3 | 0.000622 |
| 2.104000e+01 | 3 | 0.000622 |
| 3.700000e+01 | 3 | 0.000622 |
| 1.250000e+01 | 3 | 0.000622 |
| 1.803040e+03 | 3 | 0.000622 |
| 3.125200e+02 | 3 | 0.000622 |
| 6.400000e+02 | 3 | 0.000622 |
| 1.700000e+01 | 3 | 0.000622 |
| 1.320000e+03 | 3 | 0.000622 |
| 1.444000e+01 | 3 | 0.000622 |
| 4.560000e+02 | 3 | 0.000622 |
| 9.900000e+01 | 3 | 0.000622 |
| 1.650000e+02 | 3 | 0.000622 |
| 4.507600e+02 | 3 | 0.000622 |
| 2.700000e+02 | 2 | 0.000415 |
| 1.502600e+02 | 2 | 0.000415 |
| 2.884000e+01 | 2 | 0.000415 |
| 1.081200e+02 | 2 | 0.000415 |
| 3.300000e+01 | 2 | 0.000415 |
| 6.360000e+02 | 2 | 0.000415 |
| 3.666000e+01 | 2 | 0.000415 |
| 2.480000e+02 | 2 | 0.000415 |
| 7.356000e+02 | 2 | 0.000415 |
| 7.513000e+01 | 2 | 0.000415 |
| 3.365600e+02 | 2 | 0.000415 |
| 1.260000e+02 | 2 | 0.000415 |
| 1.212000e+03 | 2 | 0.000415 |
| 1.983600e+02 | 2 | 0.000415 |
| 5.772000e+01 | 2 | 0.000415 |
| 1.260000e+03 | 2 | 0.000415 |
| 9.015200e+02 | 2 | 0.000415 |
| 1.740000e+02 | 2 | 0.000415 |
| 4.600000e+02 | 2 | 0.000415 |
| 7.200000e+00 | 2 | 0.000415 |
| 3.250000e+02 | 2 | 0.000415 |
| 3.996000e+01 | 2 | 0.000415 |
| 2.300000e+01 | 2 | 0.000415 |
| 9.996000e+01 | 2 | 0.000415 |
| 5.988000e+01 | 2 | 0.000415 |
| 1.640000e+02 | 2 | 0.000415 |
| 2.884920e+03 | 2 | 0.000415 |
| 1.322400e+02 | 2 | 0.000415 |
| 3.300000e+02 | 2 | 0.000415 |
| 4.600000e+01 | 2 | 0.000415 |
| 7.200000e-01 | 2 | 0.000415 |
| 2.900000e+01 | 2 | 0.000415 |
| 1.560000e+03 | 2 | 0.000415 |
| 2.040000e+03 | 2 | 0.000415 |
| 2.163600e+03 | 2 | 0.000415 |
| 9.360000e+02 | 2 | 0.000415 |
| 1.510000e+02 | 2 | 0.000415 |
| 1.732000e+01 | 2 | 0.000415 |
| 2.004000e+03 | 2 | 0.000415 |
| 5.300000e+01 | 2 | 0.000415 |
| 2.598000e+01 | 2 | 0.000415 |
| 1.400000e+03 | 2 | 0.000415 |
| 6.611000e+01 | 2 | 0.000415 |
| 1.230000e+02 | 2 | 0.000415 |
| 1.129900e+02 | 2 | 0.000415 |
| 2.061500e+02 | 2 | 0.000415 |
| 1.900000e+02 | 2 | 0.000415 |
| 3.900000e+02 | 2 | 0.000415 |
| 1.100000e+03 | 2 | 0.000415 |
| 1.240000e+02 | 2 | 0.000415 |
| 1.100000e+01 | 2 | 0.000415 |
| 1.920000e+03 | 2 | 0.000415 |
| 6.200000e+02 | 2 | 0.000415 |
| 3.750000e+02 | 2 | 0.000415 |
| 3.400000e+02 | 2 | 0.000415 |
| 3.846400e+02 | 2 | 0.000415 |
| 6.960000e+02 | 2 | 0.000415 |
| 4.300000e+01 | 2 | 0.000415 |
| 3.660000e+02 | 2 | 0.000415 |
| 1.620000e+02 | 2 | 0.000415 |
| 5.408000e+01 | 2 | 0.000415 |
| 1.355880e+03 | 2 | 0.000415 |
| 2.750000e+02 | 2 | 0.000415 |
| 7.560000e+02 | 2 | 0.000415 |
| 4.328000e+01 | 2 | 0.000415 |
| 1.154400e+02 | 2 | 0.000415 |
| 1.586400e+02 | 2 | 0.000415 |
| 1.830000e+02 | 1 | 0.000207 |
| 9.500000e+01 | 1 | 0.000207 |
| 1.960000e+02 | 1 | 0.000207 |
| 6.015000e+01 | 1 | 0.000207 |
| 7.700000e+01 | 1 | 0.000207 |
| 1.110000e+02 | 1 | 0.000207 |
| 9.015100e+02 | 1 | 0.000207 |
| 3.350000e+02 | 1 | 0.000207 |
| 7.500000e+02 | 1 | 0.000207 |
| 5.100000e+02 | 1 | 0.000207 |
| 9.204000e+01 | 1 | 0.000207 |
| 1.716000e+02 | 1 | 0.000207 |
| 2.550000e+02 | 1 | 0.000207 |
| 9.324000e+01 | 1 | 0.000207 |
| 3.800000e+02 | 1 | 0.000207 |
| 3.040000e+01 | 1 | 0.000207 |
| 1.959600e+02 | 1 | 0.000207 |
| 4.992000e+01 | 1 | 0.000207 |
| 5.409600e+02 | 1 | 0.000207 |
| 2.240000e+02 | 1 | 0.000207 |
| 2.409600e+02 | 1 | 0.000207 |
| 2.560000e+02 | 1 | 0.000207 |
| 1.394000e+02 | 1 | 0.000207 |
| 2.880000e+01 | 1 | 0.000207 |
| 3.110000e+02 | 1 | 0.000207 |
| 1.210000e+02 | 1 | 0.000207 |
| 4.808100e+02 | 1 | 0.000207 |
| 6.100000e+01 | 1 | 0.000207 |
| 6.396000e+01 | 1 | 0.000207 |
| 5.040000e+01 | 1 | 0.000207 |
| 2.403600e+02 | 1 | 0.000207 |
| 8.656000e+01 | 1 | 0.000207 |
| 1.000800e+02 | 1 | 0.000207 |
| 7.992000e+01 | 1 | 0.000207 |
| 1.202400e+02 | 1 | 0.000207 |
| 1.250400e+02 | 1 | 0.000207 |
| 1.159200e+02 | 1 | 0.000207 |
| 2.100000e+01 | 1 | 0.000207 |
| 7.440000e+01 | 1 | 0.000207 |
| 1.900000e+01 | 1 | 0.000207 |
| 6.600000e+03 | 1 | 0.000207 |
| 4.207100e+02 | 1 | 0.000207 |
| 2.439600e+02 | 1 | 0.000207 |
| 5.880000e+02 | 1 | 0.000207 |
| 2.000000e+00 | 1 | 0.000207 |
| 4.200000e+03 | 1 | 0.000207 |
| 6.800000e+02 | 1 | 0.000207 |
| 4.700000e+01 | 1 | 0.000207 |
| 3.906600e+02 | 1 | 0.000207 |
| 2.340000e+02 | 1 | 0.000207 |
| 1.870000e+02 | 1 | 0.000207 |
| 1.658400e+02 | 1 | 0.000207 |
| 6.016000e+01 | 1 | 0.000207 |
| 2.644400e+02 | 1 | 0.000207 |
| 1.622400e+02 | 1 | 0.000207 |
| 6.972000e+01 | 1 | 0.000207 |
| 6.001000e+01 | 1 | 0.000207 |
| 4.095600e+02 | 1 | 0.000207 |
| 2.884900e+02 | 1 | 0.000207 |
| 1.947600e+02 | 1 | 0.000207 |
| 2.100000e+03 | 1 | 0.000207 |
| 1.440000e+01 | 1 | 0.000207 |
| 3.320000e+02 | 1 | 0.000207 |
| 6.720000e+01 | 1 | 0.000207 |
| 9.372000e+01 | 1 | 0.000207 |
| 1.860000e+03 | 1 | 0.000207 |
| 7.812000e+01 | 1 | 0.000207 |
| 1.470000e+02 | 1 | 0.000207 |
| 1.472500e+02 | 1 | 0.000207 |
| 2.180000e+02 | 1 | 0.000207 |
| 5.196000e+02 | 1 | 0.000207 |
| 4.100000e+01 | 1 | 0.000207 |
| 7.250000e+02 | 1 | 0.000207 |
| 9.320000e+00 | 1 | 0.000207 |
| 1.208000e+02 | 1 | 0.000207 |
| 2.050000e+02 | 1 | 0.000207 |
| 5.760000e+02 | 1 | 0.000207 |
| 4.400000e+02 | 1 | 0.000207 |
| 2.496000e+03 | 1 | 0.000207 |
| 2.476800e+02 | 1 | 0.000207 |
| 9.400000e+01 | 1 | 0.000207 |
| 1.812000e+02 | 1 | 0.000207 |
| 8.246000e+02 | 1 | 0.000207 |
| 1.036800e+02 | 1 | 0.000207 |
| 3.780000e+02 | 1 | 0.000207 |
| 4.330000e+00 | 1 | 0.000207 |
| 6.492000e+01 | 1 | 0.000207 |
| 1.268400e+02 | 1 | 0.000207 |
| 2.850000e+02 | 1 | 0.000207 |
| 1.420000e+02 | 1 | 0.000207 |
| 1.010000e+02 | 1 | 0.000207 |
| 1.200100e+02 | 1 | 0.000207 |
| 3.607200e+02 | 1 | 0.000207 |
| 8.700000e+01 | 1 | 0.000207 |
| 1.056000e+02 | 1 | 0.000207 |
| 1.300000e+03 | 1 | 0.000207 |
| 4.116000e+01 | 1 | 0.000207 |
| 6.660000e+01 | 1 | 0.000207 |
| 2.000400e+02 | 1 | 0.000207 |
| 1.678800e+02 | 1 | 0.000207 |
| 9.100000e+01 | 1 | 0.000207 |
| 2.760000e+03 | 1 | 0.000207 |
| 1.502530e+03 | 1 | 0.000207 |
| 4.182000e+02 | 1 | 0.000207 |
| 9.600000e+03 | 1 | 0.000207 |
| 2.500000e+03 | 1 | 0.000207 |
| 7.596000e+01 | 1 | 0.000207 |
| 9.012000e+02 | 1 | 0.000207 |
| 6.720000e+02 | 1 | 0.000207 |
| 8.160000e+02 | 1 | 0.000207 |
| 1.060000e+02 | 1 | 0.000207 |
| 1.203000e+01 | 1 | 0.000207 |
| 1.850000e+02 | 1 | 0.000207 |
| 2.524400e+02 | 1 | 0.000207 |
| 1.239600e+02 | 1 | 0.000207 |
| 4.800000e+00 | 1 | 0.000207 |
| 1.812000e+03 | 1 | 0.000207 |
| 3.100000e+02 | 1 | 0.000207 |
| 1.046400e+02 | 1 | 0.000207 |
| 4.327000e+01 | 1 | 0.000207 |
| 4.580000e+02 | 1 | 0.000207 |
| 2.705000e+01 | 1 | 0.000207 |
| 3.330000e+02 | 1 | 0.000207 |
| 1.202000e+03 | 1 | 0.000207 |
| 9.496000e+01 | 1 | 0.000207 |
| 1.802800e+02 | 1 | 0.000207 |
| 1.382000e+01 | 1 | 0.000207 |
| 1.226400e+02 | 1 | 0.000207 |
| 1.009200e+02 | 1 | 0.000207 |
| 1.800000e+04 | 1 | 0.000207 |
| 1.620000e+03 | 1 | 0.000207 |
| 1.840000e+02 | 1 | 0.000207 |
| 2.456676e+07 | 1 | 0.000207 |
| 4.320000e+03 | 1 | 0.000207 |
| 1.322200e+02 | 1 | 0.000207 |
| 1.710000e+02 | 1 | 0.000207 |
| 6.132000e+01 | 1 | 0.000207 |
| 2.220000e+02 | 1 | 0.000207 |
| 1.430400e+02 | 1 | 0.000207 |
| 2.596800e+02 | 1 | 0.000207 |
| 8.292000e+01 | 1 | 0.000207 |
| 1.460000e+02 | 1 | 0.000207 |
| 5.520000e+02 | 1 | 0.000207 |
| 1.382300e+02 | 1 | 0.000207 |
| 1.983300e+02 | 1 | 0.000207 |
| 4.028000e+01 | 1 | 0.000207 |
| 7.572000e+01 | 1 | 0.000207 |
| 3.065200e+02 | 1 | 0.000207 |
| 2.043600e+02 | 1 | 0.000207 |
| 9.020000e+01 | 1 | 0.000207 |
| 6.010200e+02 | 1 | 0.000207 |
| 7.212200e+02 | 1 | 0.000207 |
| 1.250000e+03 | 1 | 0.000207 |
| 1.562800e+02 | 1 | 0.000207 |
| 1.684000e+01 | 1 | 0.000207 |
| 3.246000e+02 | 1 | 0.000207 |
| 8.052648e+09 | 1 | 0.000207 |
| 2.880000e+03 | 1 | 0.000207 |
| 5.412000e+01 | 1 | 0.000207 |
| 1.980000e+02 | 1 | 0.000207 |
| 3.006000e+01 | 1 | 0.000207 |
| 3.230000e+02 | 1 | 0.000207 |
| 1.201200e+04 | 1 | 0.000207 |
| 7.204000e+01 | 1 | 0.000207 |
| 1.983200e+02 | 1 | 0.000207 |
| 1.008000e+03 | 1 | 0.000207 |
| 8.280000e+02 | 1 | 0.000207 |
| 2.900000e+02 | 1 | 0.000207 |
| 1.188000e+02 | 1 | 0.000207 |
| 4.000000e+03 | 1 | 0.000207 |
| 4.700000e+02 | 1 | 0.000207 |
| 6.480000e+02 | 1 | 0.000207 |
| 7.320000e+02 | 1 | 0.000207 |
| 2.401200e+02 | 1 | 0.000207 |
| 2.170800e+02 | 1 | 0.000207 |
| 4.900000e+01 | 1 | 0.000207 |
| 7.080000e+02 | 1 | 0.000207 |
| 2.410000e+02 | 1 | 0.000207 |
| 3.004000e+01 | 1 | 0.000207 |
| 5.152800e+02 | 1 | 0.000207 |
| 1.056000e+03 | 1 | 0.000207 |
| 4.920000e+02 | 1 | 0.000207 |
| 3.060000e+02 | 1 | 0.000207 |
| 5.500000e+02 | 1 | 0.000207 |
| 3.700000e+02 | 1 | 0.000207 |
| 3.604000e+01 | 1 | 0.000207 |
| 1.490400e+02 | 1 | 0.000207 |
| 8.900000e+01 | 1 | 0.000207 |
| 1.000100e+02 | 1 | 0.000207 |
| 1.211640e+03 | 1 | 0.000207 |
| 1.340000e+01 | 1 | 0.000207 |
| 8.655000e+01 | 1 | 0.000207 |
| 2.451600e+02 | 1 | 0.000207 |
| 1.202020e+03 | 1 | 0.000207 |
| 1.021700e+02 | 1 | 0.000207 |
| 1.501500e+02 | 1 | 0.000207 |
| 7.320000e+01 | 1 | 0.000207 |
| 1.500100e+02 | 1 | 0.000207 |
| 6.020000e+02 | 1 | 0.000207 |
| 1.146720e+03 | 1 | 0.000207 |
| 1.802400e+02 | 1 | 0.000207 |
| 3.607000e+01 | 1 | 0.000207 |
| 1.875600e+02 | 1 | 0.000207 |
| 2.019600e+02 | 1 | 0.000207 |
| 9.720000e+02 | 1 | 0.000207 |
| 1.599600e+02 | 1 | 0.000207 |
| 2.164000e+01 | 1 | 0.000207 |
| 1.444800e+02 | 1 | 0.000207 |
| 3.602400e+02 | 1 | 0.000207 |
| 2.440000e+02 | 1 | 0.000207 |
| 3.900000e+03 | 1 | 0.000207 |
| 4.520000e+02 | 1 | 0.000207 |
| 2.046000e+02 | 1 | 0.000207 |
| 5.950000e+01 | 1 | 0.000207 |
| 1.092000e+02 | 1 | 0.000207 |
| 2.282400e+03 | 1 | 0.000207 |
| 1.220000e+02 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_program__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_program__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_program__c es 129. Lo que supone un 0.026751053452337505%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Cultivación socios MASS | 432993 | 89.790844 |
| Retención 1r año MASS | 24150 | 5.008046 |
| Cultivación socios MID | 17102 | 3.546485 |
| Mid+ Donors | 4248 | 0.880918 |
| Empresas y Colectivos Mass | 2262 | 0.469077 |
| Testamentarios | 624 | 0.129400 |
| Retención 1r año MID | 188 | 0.038986 |
| Otros programas transversales | 134 | 0.027788 |
| 129 | 0.026751 | |
| Otros 12Few+ | 116 | 0.024055 |
| Empresas y Colectivos Mid, Mid + | 83 | 0.017212 |
| Públicos Especiales | 65 | 0.013479 |
| Potenciales a Major Donors | 52 | 0.010783 |
| Major Donors | 36 | 0.007465 |
| Empresas y Colectivos Estratégicas | 16 | 0.003318 |
| Instituciones Públicas Mass | 12 | 0.002488 |
| Fundaciones Mass | 7 | 0.001452 |
| Reactivación bajas MASS | 3 | 0.000622 |
| Cultivación/conversión Donantes MASS | 2 | 0.000415 |
| Fundaciones Mid, Mid + | 1 | 0.000207 |
| Reactivación/conversión EXDonantes MASS | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_programaherencias__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_programaherencias__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 478318 | 99.190003 |
| True | 3906 | 0.809997 |
# Vamos a realizar analisis por cada variable
var = "msf_programais__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_programais__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_programais__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 481983 | 99.950023 |
| True | 241 | 0.049977 |
# Vamos a realizar analisis por cada variable
var = "msf_pressurecomplaint__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 479806 | 99.498573 |
| True | 2418 | 0.501427 |
# Vamos a realizar analisis por cada variable
var = "msf_recencydonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencydonorcont__c es 301314. Lo que supone un 62.48423968943894% El nº de vacios para la variable msf_recencydonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 218.0 | 6014 | 3.324305 |
| 128.0 | 5548 | 3.066718 |
| 1102.0 | 4623 | 2.555414 |
| 4.0 | 3227 | 1.783760 |
| 583.0 | 3014 | 1.666022 |
| ... | ... | ... |
| 10389.0 | 1 | 0.000553 |
| 6575.0 | 1 | 0.000553 |
| 4871.0 | 1 | 0.000553 |
| 2516.0 | 1 | 0.000553 |
| 5105.0 | 1 | 0.000553 |
6531 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_recencyrecurringdonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencyrecurringdonorcont__c es 868. Lo que supone un 0.17999933640797636% El nº de vacios para la variable msf_recencyrecurringdonorcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4.0 | 391165 | 81.263140 |
| 36.0 | 18626 | 3.869485 |
| 66.0 | 17367 | 3.607933 |
| 156.0 | 9823 | 2.040693 |
| 186.0 | 9781 | 2.031968 |
| 128.0 | 7009 | 1.456095 |
| 218.0 | 6613 | 1.373827 |
| 95.0 | 5744 | 1.193296 |
| 247.0 | 4378 | 0.909514 |
| 340.0 | 4108 | 0.853422 |
| 277.0 | 3628 | 0.753704 |
| 309.0 | 2675 | 0.555722 |
| 583.0 | 25 | 0.005194 |
| 401.0 | 23 | 0.004778 |
| 550.0 | 23 | 0.004778 |
| 368.0 | 22 | 0.004570 |
| 462.0 | 21 | 0.004363 |
| 644.0 | 20 | 0.004155 |
| 704.0 | 16 | 0.003324 |
| 493.0 | 15 | 0.003116 |
| 431.0 | 12 | 0.002493 |
| 521.0 | 11 | 0.002285 |
| 612.0 | 11 | 0.002285 |
| 674.0 | 11 | 0.002285 |
| 2012.0 | 7 | 0.001454 |
| 1069.0 | 6 | 0.001246 |
| 1283.0 | 6 | 0.001246 |
| 1251.0 | 6 | 0.001246 |
| 1618.0 | 6 | 0.001246 |
| 5.0 | 6 | 0.001246 |
| 1102.0 | 5 | 0.001039 |
| 1192.0 | 5 | 0.001039 |
| 1678.0 | 5 | 0.001039 |
| 1009.0 | 5 | 0.001039 |
| 1922.0 | 4 | 0.000831 |
| 1342.0 | 4 | 0.000831 |
| 976.0 | 4 | 0.000831 |
| 1437.0 | 4 | 0.000831 |
| 2196.0 | 3 | 0.000623 |
| 1863.0 | 3 | 0.000623 |
| 2501.0 | 3 | 0.000623 |
| 1832.0 | 3 | 0.000623 |
| 2867.0 | 3 | 0.000623 |
| 1040.0 | 3 | 0.000623 |
| 1375.0 | 3 | 0.000623 |
| 1131.0 | 3 | 0.000623 |
| 2042.0 | 3 | 0.000623 |
| 736.0 | 3 | 0.000623 |
| 2105.0 | 3 | 0.000623 |
| 1223.0 | 3 | 0.000623 |
| 3474.0 | 3 | 0.000623 |
| 1496.0 | 3 | 0.000623 |
| 2804.0 | 3 | 0.000623 |
| 1802.0 | 3 | 0.000623 |
| 2347.0 | 2 | 0.000415 |
| 2987.0 | 2 | 0.000415 |
| 1161.0 | 2 | 0.000415 |
| 2439.0 | 2 | 0.000415 |
| 2378.0 | 2 | 0.000415 |
| 914.0 | 2 | 0.000415 |
| 2469.0 | 2 | 0.000415 |
| 1648.0 | 2 | 0.000415 |
| 3505.0 | 2 | 0.000415 |
| 3566.0 | 2 | 0.000415 |
| 3869.0 | 2 | 0.000415 |
| 2287.0 | 2 | 0.000415 |
| 1740.0 | 2 | 0.000415 |
| 948.0 | 2 | 0.000415 |
| 2714.0 | 2 | 0.000415 |
| 3414.0 | 2 | 0.000415 |
| 2258.0 | 2 | 0.000415 |
| 3687.0 | 2 | 0.000415 |
| 1559.0 | 2 | 0.000415 |
| 37.0 | 2 | 0.000415 |
| 766.0 | 2 | 0.000415 |
| 2685.0 | 2 | 0.000415 |
| 2074.0 | 2 | 0.000415 |
| 1769.0 | 2 | 0.000415 |
| 3351.0 | 2 | 0.000415 |
| 2167.0 | 2 | 0.000415 |
| 2623.0 | 1 | 0.000208 |
| 3627.0 | 1 | 0.000208 |
| 2532.0 | 1 | 0.000208 |
| 2990.0 | 1 | 0.000208 |
| 3320.0 | 1 | 0.000208 |
| 4569.0 | 1 | 0.000208 |
| 1894.0 | 1 | 0.000208 |
| 1314.0 | 1 | 0.000208 |
| 3293.0 | 1 | 0.000208 |
| 1709.0 | 1 | 0.000208 |
| 6305.0 | 1 | 0.000208 |
| 3140.0 | 1 | 0.000208 |
| 3659.0 | 1 | 0.000208 |
| 3442.0 | 1 | 0.000208 |
| 4755.0 | 1 | 0.000208 |
| 886.0 | 1 | 0.000208 |
| 2593.0 | 1 | 0.000208 |
| 5210.0 | 1 | 0.000208 |
| 4083.0 | 1 | 0.000208 |
| 3960.0 | 1 | 0.000208 |
| 858.0 | 1 | 0.000208 |
| 5819.0 | 1 | 0.000208 |
| 1590.0 | 1 | 0.000208 |
| 2654.0 | 1 | 0.000208 |
| 2563.0 | 1 | 0.000208 |
| 5268.0 | 1 | 0.000208 |
| 4933.0 | 1 | 0.000208 |
| 1955.0 | 1 | 0.000208 |
| 2136.0 | 1 | 0.000208 |
| 795.0 | 1 | 0.000208 |
| 1528.0 | 1 | 0.000208 |
| 1771.0 | 1 | 0.000208 |
| 219.0 | 1 | 0.000208 |
| 1892.0 | 1 | 0.000208 |
| 4814.0 | 1 | 0.000208 |
| 2775.0 | 1 | 0.000208 |
| 4358.0 | 1 | 0.000208 |
| 4146.0 | 1 | 0.000208 |
| 3749.0 | 1 | 0.000208 |
| 5057.0 | 1 | 0.000208 |
| 3416.0 | 1 | 0.000208 |
| 2837.0 | 1 | 0.000208 |
| 3839.0 | 1 | 0.000208 |
| 4512.0 | 1 | 0.000208 |
| 1468.0 | 1 | 0.000208 |
| 4723.0 | 1 | 0.000208 |
| 6245.0 | 1 | 0.000208 |
| 2410.0 | 1 | 0.000208 |
| 4784.0 | 1 | 0.000208 |
| 3078.0 | 1 | 0.000208 |
| 1983.0 | 1 | 0.000208 |
| 3050.0 | 1 | 0.000208 |
| 4877.0 | 1 | 0.000208 |
| 3781.0 | 1 | 0.000208 |
| 5027.0 | 1 | 0.000208 |
| 4295.0 | 1 | 0.000208 |
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencytotalcont__c es 825. Lo que supone un 0.17108231859053055% El nº de vacios para la variable msf_recencytotalcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4.0 | 391902 | 81.408977 |
| 36.0 | 18702 | 3.884927 |
| 66.0 | 17452 | 3.625267 |
| 156.0 | 9217 | 1.914628 |
| 186.0 | 8988 | 1.867058 |
| 128.0 | 7433 | 1.544041 |
| 218.0 | 6244 | 1.297053 |
| 95.0 | 5745 | 1.193397 |
| 247.0 | 3905 | 0.811177 |
| 340.0 | 3683 | 0.765062 |
| 277.0 | 3285 | 0.682386 |
| 309.0 | 2420 | 0.502702 |
| 127.0 | 331 | 0.068758 |
| 150.0 | 151 | 0.031367 |
| 149.0 | 132 | 0.027420 |
| 198.0 | 110 | 0.022850 |
| 151.0 | 106 | 0.022019 |
| 148.0 | 73 | 0.015164 |
| 152.0 | 60 | 0.012464 |
| 145.0 | 48 | 0.009971 |
| 143.0 | 33 | 0.006855 |
| 24.0 | 30 | 0.006232 |
| 3.0 | 29 | 0.006024 |
| 147.0 | 29 | 0.006024 |
| 146.0 | 27 | 0.005609 |
| 144.0 | 24 | 0.004985 |
| 142.0 | 23 | 0.004778 |
| 401.0 | 22 | 0.004570 |
| 5.0 | 22 | 0.004570 |
| 19.0 | 22 | 0.004570 |
| 78.0 | 21 | 0.004362 |
| 25.0 | 20 | 0.004155 |
| 368.0 | 20 | 0.004155 |
| 550.0 | 19 | 0.003947 |
| 583.0 | 19 | 0.003947 |
| 462.0 | 19 | 0.003947 |
| 23.0 | 18 | 0.003739 |
| 26.0 | 18 | 0.003739 |
| 644.0 | 18 | 0.003739 |
| 39.0 | 16 | 0.003324 |
| 18.0 | 16 | 0.003324 |
| 141.0 | 16 | 0.003324 |
| 704.0 | 15 | 0.003116 |
| 22.0 | 15 | 0.003116 |
| 138.0 | 14 | 0.002908 |
| 2.0 | 14 | 0.002908 |
| 493.0 | 14 | 0.002908 |
| 40.0 | 13 | 0.002700 |
| 17.0 | 12 | 0.002493 |
| 674.0 | 12 | 0.002493 |
| 12.0 | 12 | 0.002493 |
| 75.0 | 12 | 0.002493 |
| 521.0 | 12 | 0.002493 |
| 431.0 | 11 | 0.002285 |
| 99.0 | 11 | 0.002285 |
| 29.0 | 11 | 0.002285 |
| 612.0 | 11 | 0.002285 |
| 11.0 | 10 | 0.002077 |
| 96.0 | 9 | 0.001870 |
| 1102.0 | 8 | 0.001662 |
| 193.0 | 8 | 0.001662 |
| 15.0 | 8 | 0.001662 |
| 137.0 | 8 | 0.001662 |
| 38.0 | 8 | 0.001662 |
| 10.0 | 8 | 0.001662 |
| 194.0 | 8 | 0.001662 |
| 37.0 | 8 | 0.001662 |
| 184.0 | 7 | 0.001454 |
| 85.0 | 7 | 0.001454 |
| 242.0 | 7 | 0.001454 |
| 190.0 | 7 | 0.001454 |
| 134.0 | 7 | 0.001454 |
| 8.0 | 7 | 0.001454 |
| 16.0 | 7 | 0.001454 |
| 77.0 | 7 | 0.001454 |
| 197.0 | 7 | 0.001454 |
| 191.0 | 7 | 0.001454 |
| 74.0 | 6 | 0.001246 |
| 1283.0 | 6 | 0.001246 |
| 87.0 | 6 | 0.001246 |
| 1618.0 | 6 | 0.001246 |
| 192.0 | 6 | 0.001246 |
| 30.0 | 6 | 0.001246 |
| 1069.0 | 6 | 0.001246 |
| 243.0 | 6 | 0.001246 |
| 71.0 | 6 | 0.001246 |
| 1251.0 | 6 | 0.001246 |
| 136.0 | 6 | 0.001246 |
| 187.0 | 6 | 0.001246 |
| 189.0 | 6 | 0.001246 |
| 89.0 | 5 | 0.001039 |
| 140.0 | 5 | 0.001039 |
| 70.0 | 5 | 0.001039 |
| 1678.0 | 5 | 0.001039 |
| 139.0 | 5 | 0.001039 |
| 199.0 | 5 | 0.001039 |
| 27.0 | 5 | 0.001039 |
| 213.0 | 5 | 0.001039 |
| 1.0 | 5 | 0.001039 |
| 94.0 | 5 | 0.001039 |
| 204.0 | 5 | 0.001039 |
| 88.0 | 5 | 0.001039 |
| 123.0 | 5 | 0.001039 |
| 179.0 | 4 | 0.000831 |
| 47.0 | 4 | 0.000831 |
| 43.0 | 4 | 0.000831 |
| 211.0 | 4 | 0.000831 |
| 1192.0 | 4 | 0.000831 |
| 100.0 | 4 | 0.000831 |
| 135.0 | 4 | 0.000831 |
| 1040.0 | 4 | 0.000831 |
| 130.0 | 4 | 0.000831 |
| 1342.0 | 4 | 0.000831 |
| 64.0 | 4 | 0.000831 |
| 58.0 | 4 | 0.000831 |
| 68.0 | 4 | 0.000831 |
| 65.0 | 4 | 0.000831 |
| 219.0 | 4 | 0.000831 |
| 222.0 | 4 | 0.000831 |
| 67.0 | 4 | 0.000831 |
| 2012.0 | 4 | 0.000831 |
| 9.0 | 4 | 0.000831 |
| 1009.0 | 4 | 0.000831 |
| 205.0 | 3 | 0.000623 |
| 2501.0 | 3 | 0.000623 |
| 1922.0 | 3 | 0.000623 |
| 217.0 | 3 | 0.000623 |
| 1132.0 | 3 | 0.000623 |
| 117.0 | 3 | 0.000623 |
| 976.0 | 3 | 0.000623 |
| 176.0 | 3 | 0.000623 |
| 736.0 | 3 | 0.000623 |
| 180.0 | 3 | 0.000623 |
| 112.0 | 3 | 0.000623 |
| 2804.0 | 3 | 0.000623 |
| 304.0 | 3 | 0.000623 |
| 208.0 | 3 | 0.000623 |
| 13.0 | 3 | 0.000623 |
| 201.0 | 3 | 0.000623 |
| 1437.0 | 3 | 0.000623 |
| 129.0 | 3 | 0.000623 |
| 1496.0 | 3 | 0.000623 |
| 164.0 | 3 | 0.000623 |
| 1131.0 | 3 | 0.000623 |
| 200.0 | 3 | 0.000623 |
| 32.0 | 3 | 0.000623 |
| 131.0 | 3 | 0.000623 |
| 1223.0 | 3 | 0.000623 |
| 14.0 | 3 | 0.000623 |
| 20.0 | 3 | 0.000623 |
| 2196.0 | 3 | 0.000623 |
| 174.0 | 3 | 0.000623 |
| 225.0 | 3 | 0.000623 |
| 101.0 | 3 | 0.000623 |
| 2439.0 | 2 | 0.000415 |
| 2167.0 | 2 | 0.000415 |
| 1863.0 | 2 | 0.000415 |
| 183.0 | 2 | 0.000415 |
| 1740.0 | 2 | 0.000415 |
| 2685.0 | 2 | 0.000415 |
| 73.0 | 2 | 0.000415 |
| 216.0 | 2 | 0.000415 |
| 3414.0 | 2 | 0.000415 |
| 196.0 | 2 | 0.000415 |
| 116.0 | 2 | 0.000415 |
| 46.0 | 2 | 0.000415 |
| 122.0 | 2 | 0.000415 |
| 2105.0 | 2 | 0.000415 |
| 3869.0 | 2 | 0.000415 |
| 914.0 | 2 | 0.000415 |
| 80.0 | 2 | 0.000415 |
| 42.0 | 2 | 0.000415 |
| 102.0 | 2 | 0.000415 |
| 35.0 | 2 | 0.000415 |
| 519.0 | 2 | 0.000415 |
| 202.0 | 2 | 0.000415 |
| 215.0 | 2 | 0.000415 |
| 166.0 | 2 | 0.000415 |
| 1648.0 | 2 | 0.000415 |
| 124.0 | 2 | 0.000415 |
| 3505.0 | 2 | 0.000415 |
| 210.0 | 2 | 0.000415 |
| 2347.0 | 2 | 0.000415 |
| 234.0 | 2 | 0.000415 |
| 33.0 | 2 | 0.000415 |
| 1161.0 | 2 | 0.000415 |
| 3687.0 | 2 | 0.000415 |
| 2378.0 | 2 | 0.000415 |
| 203.0 | 2 | 0.000415 |
| 766.0 | 2 | 0.000415 |
| 61.0 | 2 | 0.000415 |
| 86.0 | 2 | 0.000415 |
| 28.0 | 2 | 0.000415 |
| 206.0 | 2 | 0.000415 |
| 1071.0 | 2 | 0.000415 |
| 3351.0 | 2 | 0.000415 |
| 93.0 | 2 | 0.000415 |
| 195.0 | 2 | 0.000415 |
| 21.0 | 2 | 0.000415 |
| 81.0 | 2 | 0.000415 |
| 261.0 | 2 | 0.000415 |
| 31.0 | 2 | 0.000415 |
| 1802.0 | 2 | 0.000415 |
| 57.0 | 2 | 0.000415 |
| 3474.0 | 2 | 0.000415 |
| 240.0 | 2 | 0.000415 |
| 44.0 | 2 | 0.000415 |
| 1559.0 | 2 | 0.000415 |
| 132.0 | 2 | 0.000415 |
| 98.0 | 2 | 0.000415 |
| 275.0 | 2 | 0.000415 |
| 6.0 | 2 | 0.000415 |
| 178.0 | 2 | 0.000415 |
| 185.0 | 2 | 0.000415 |
| 293.0 | 2 | 0.000415 |
| 269.0 | 2 | 0.000415 |
| 172.0 | 2 | 0.000415 |
| 173.0 | 2 | 0.000415 |
| 2714.0 | 2 | 0.000415 |
| 1375.0 | 2 | 0.000415 |
| 221.0 | 2 | 0.000415 |
| 227.0 | 2 | 0.000415 |
| 165.0 | 2 | 0.000415 |
| 2867.0 | 2 | 0.000415 |
| 76.0 | 2 | 0.000415 |
| 2469.0 | 1 | 0.000208 |
| 2987.0 | 1 | 0.000208 |
| 566.0 | 1 | 0.000208 |
| 590.0 | 1 | 0.000208 |
| 246.0 | 1 | 0.000208 |
| 1894.0 | 1 | 0.000208 |
| 155.0 | 1 | 0.000208 |
| 153.0 | 1 | 0.000208 |
| 264.0 | 1 | 0.000208 |
| 2532.0 | 1 | 0.000208 |
| 103.0 | 1 | 0.000208 |
| 7.0 | 1 | 0.000208 |
| 4083.0 | 1 | 0.000208 |
| 3960.0 | 1 | 0.000208 |
| 407.0 | 1 | 0.000208 |
| 1955.0 | 1 | 0.000208 |
| 162.0 | 1 | 0.000208 |
| 5210.0 | 1 | 0.000208 |
| 1173.0 | 1 | 0.000208 |
| 3293.0 | 1 | 0.000208 |
| 1709.0 | 1 | 0.000208 |
| 858.0 | 1 | 0.000208 |
| 253.0 | 1 | 0.000208 |
| 522.0 | 1 | 0.000208 |
| 4569.0 | 1 | 0.000208 |
| 3320.0 | 1 | 0.000208 |
| 1769.0 | 1 | 0.000208 |
| 2623.0 | 1 | 0.000208 |
| 2990.0 | 1 | 0.000208 |
| 119.0 | 1 | 0.000208 |
| 157.0 | 1 | 0.000208 |
| 3659.0 | 1 | 0.000208 |
| 3442.0 | 1 | 0.000208 |
| 229.0 | 1 | 0.000208 |
| 280.0 | 1 | 0.000208 |
| 486.0 | 1 | 0.000208 |
| 4755.0 | 1 | 0.000208 |
| 528.0 | 1 | 0.000208 |
| 52.0 | 1 | 0.000208 |
| 948.0 | 1 | 0.000208 |
| 274.0 | 1 | 0.000208 |
| 5268.0 | 1 | 0.000208 |
| 886.0 | 1 | 0.000208 |
| 1284.0 | 1 | 0.000208 |
| 4933.0 | 1 | 0.000208 |
| 113.0 | 1 | 0.000208 |
| 489.0 | 1 | 0.000208 |
| 257.0 | 1 | 0.000208 |
| 3190.0 | 1 | 0.000208 |
| 3627.0 | 1 | 0.000208 |
| 72.0 | 1 | 0.000208 |
| 41.0 | 1 | 0.000208 |
| 3140.0 | 1 | 0.000208 |
| 5819.0 | 1 | 0.000208 |
| 34.0 | 1 | 0.000208 |
| 526.0 | 1 | 0.000208 |
| 231.0 | 1 | 0.000208 |
| 922.0 | 1 | 0.000208 |
| 284.0 | 1 | 0.000208 |
| 154.0 | 1 | 0.000208 |
| 1314.0 | 1 | 0.000208 |
| 5027.0 | 1 | 0.000208 |
| 171.0 | 1 | 0.000208 |
| 2042.0 | 1 | 0.000208 |
| 1468.0 | 1 | 0.000208 |
| 163.0 | 1 | 0.000208 |
| 4723.0 | 1 | 0.000208 |
| 484.0 | 1 | 0.000208 |
| 2031.0 | 1 | 0.000208 |
| 302.0 | 1 | 0.000208 |
| 79.0 | 1 | 0.000208 |
| 2530.0 | 1 | 0.000208 |
| 1832.0 | 1 | 0.000208 |
| 159.0 | 1 | 0.000208 |
| 1528.0 | 1 | 0.000208 |
| 133.0 | 1 | 0.000208 |
| 339.0 | 1 | 0.000208 |
| 4784.0 | 1 | 0.000208 |
| 563.0 | 1 | 0.000208 |
| 4512.0 | 1 | 0.000208 |
| 233.0 | 1 | 0.000208 |
| 1108.0 | 1 | 0.000208 |
| 497.0 | 1 | 0.000208 |
| 308.0 | 1 | 0.000208 |
| 3839.0 | 1 | 0.000208 |
| 494.0 | 1 | 0.000208 |
| 1590.0 | 1 | 0.000208 |
| 5057.0 | 1 | 0.000208 |
| 3749.0 | 1 | 0.000208 |
| 4146.0 | 1 | 0.000208 |
| 54.0 | 1 | 0.000208 |
| 53.0 | 1 | 0.000208 |
| 4358.0 | 1 | 0.000208 |
| 1892.0 | 1 | 0.000208 |
| 479.0 | 1 | 0.000208 |
| 177.0 | 1 | 0.000208 |
| 121.0 | 1 | 0.000208 |
| 2136.0 | 1 | 0.000208 |
| 175.0 | 1 | 0.000208 |
| 2074.0 | 1 | 0.000208 |
| 244.0 | 1 | 0.000208 |
| 188.0 | 1 | 0.000208 |
| 552.0 | 1 | 0.000208 |
| 3781.0 | 1 | 0.000208 |
| 2775.0 | 1 | 0.000208 |
| 383.0 | 1 | 0.000208 |
| 170.0 | 1 | 0.000208 |
| 3416.0 | 1 | 0.000208 |
| 2654.0 | 1 | 0.000208 |
| 3566.0 | 1 | 0.000208 |
| 6305.0 | 1 | 0.000208 |
| 3506.0 | 1 | 0.000208 |
| 2258.0 | 1 | 0.000208 |
| 795.0 | 1 | 0.000208 |
| 90.0 | 1 | 0.000208 |
| 239.0 | 1 | 0.000208 |
| 4877.0 | 1 | 0.000208 |
| 169.0 | 1 | 0.000208 |
| 235.0 | 1 | 0.000208 |
| 266.0 | 1 | 0.000208 |
| 115.0 | 1 | 0.000208 |
| 82.0 | 1 | 0.000208 |
| 167.0 | 1 | 0.000208 |
| 1983.0 | 1 | 0.000208 |
| 59.0 | 1 | 0.000208 |
| 97.0 | 1 | 0.000208 |
| 181.0 | 1 | 0.000208 |
| 106.0 | 1 | 0.000208 |
| 126.0 | 1 | 0.000208 |
| 3078.0 | 1 | 0.000208 |
| 120.0 | 1 | 0.000208 |
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalscore__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_recencytotalscore__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_recencytotalscore__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 5.0 | 480989 | 99.743895 |
| 0.0 | 825 | 0.171082 |
| 4.0 | 209 | 0.043341 |
| 3.0 | 106 | 0.021981 |
| 2.0 | 70 | 0.014516 |
| 1.0 | 25 | 0.005184 |
# Vamos a realizar analisis por cada variable
var = "msf_percomssummary__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_percomssummary__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_percomssummary__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Todo | 346210 | 71.794436 |
| Varios | 103631 | 21.490220 |
| No captación de fondos | 24698 | 5.121686 |
| Nada | 7677 | 1.591999 |
| Sólo certificado fiscal | 8 | 0.001659 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 301314 | 62.484240 |
| 1.4 | 19513 | 4.046460 |
| 1.8 | 17403 | 3.608904 |
| 1.2 | 15442 | 3.202246 |
| 1.6 | 12404 | 2.572249 |
| 2.3 | 10131 | 2.100891 |
| 1.7 | 9557 | 1.981859 |
| 1.0 | 9392 | 1.947643 |
| 1.5 | 9153 | 1.898081 |
| 1.9 | 8442 | 1.750639 |
| 2.1 | 6815 | 1.413244 |
| 2.8 | 5418 | 1.123544 |
| 2.2 | 5227 | 1.083936 |
| 2.6 | 4299 | 0.891494 |
| 2.4 | 4194 | 0.869720 |
| 2.0 | 4192 | 0.869306 |
| 3.8 | 2895 | 0.600343 |
| 3.3 | 2873 | 0.595781 |
| 2.5 | 2777 | 0.575873 |
| 3.0 | 2717 | 0.563431 |
| 3.2 | 2685 | 0.556795 |
| 4.1 | 2662 | 0.552026 |
| 3.6 | 2536 | 0.525897 |
| 2.7 | 2439 | 0.505782 |
| 2.9 | 2009 | 0.416611 |
| 3.4 | 1962 | 0.406865 |
| 3.1 | 1807 | 0.374722 |
| 3.9 | 1783 | 0.369745 |
| 3.5 | 1711 | 0.354814 |
| 3.7 | 1395 | 0.289285 |
| 4.4 | 1217 | 0.252372 |
| 4.2 | 749 | 0.155322 |
| 4.3 | 705 | 0.146198 |
| 4.0 | 655 | 0.135829 |
| 4.6 | 625 | 0.129608 |
| 4.7 | 468 | 0.097050 |
| 4.9 | 402 | 0.083364 |
| 4.8 | 364 | 0.075484 |
| 1.3 | 307 | 0.063663 |
| 5.1 | 279 | 0.057857 |
| 4.5 | 275 | 0.057027 |
| 5.0 | 235 | 0.048733 |
| 5.2 | 171 | 0.035461 |
| 5.4 | 128 | 0.026544 |
| 5.5 | 107 | 0.022189 |
| 6.0 | 73 | 0.015138 |
| 5.7 | 62 | 0.012857 |
| 5.3 | 58 | 0.012028 |
| 5.6 | 52 | 0.010783 |
| 5.9 | 42 | 0.008710 |
| 6.5 | 23 | 0.004770 |
| 5.8 | 23 | 0.004770 |
| 0.8 | 13 | 0.002696 |
| 6.2 | 10 | 0.002074 |
| 0.4 | 7 | 0.001452 |
| 0.6 | 6 | 0.001244 |
| 6.1 | 4 | 0.000829 |
| 6.7 | 3 | 0.000622 |
| 6.4 | 3 | 0.000622 |
| 1.1 | 2 | 0.000415 |
| 0.5 | 2 | 0.000415 |
| 0.7 | 2 | 0.000415 |
| 6.3 | 2 | 0.000415 |
| 7.0 | 1 | 0.000207 |
| 6.8 | 1 | 0.000207 |
| 0.2 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 5.0 | 131138 | 27.194416 |
| 4.5 | 97173 | 20.151009 |
| 3.5 | 76494 | 15.862753 |
| 3.0 | 38252 | 7.932413 |
| 4.7 | 20535 | 4.258394 |
| 4.2 | 18389 | 3.813373 |
| 2.0 | 15835 | 3.283744 |
| 3.2 | 13232 | 2.743953 |
| 5.5 | 11441 | 2.372549 |
| 4.0 | 11273 | 2.337710 |
| 4.4 | 9403 | 1.949924 |
| 3.9 | 8215 | 1.703565 |
| 2.9 | 5830 | 1.208982 |
| 1.5 | 5362 | 1.111931 |
| 2.7 | 5153 | 1.068591 |
| 2.4 | 2281 | 0.473017 |
| 6.0 | 2103 | 0.436104 |
| 3.7 | 1697 | 0.351911 |
| 3.6 | 1357 | 0.281404 |
| 4.1 | 1343 | 0.278501 |
| 2.5 | 1166 | 0.241796 |
| 2.6 | 1061 | 0.220022 |
| 3.4 | 753 | 0.156151 |
| 5.2 | 752 | 0.155944 |
| 2.1 | 556 | 0.115299 |
| 4.9 | 371 | 0.076935 |
| 1.0 | 210 | 0.043548 |
| 3.1 | 146 | 0.030276 |
| 5.7 | 143 | 0.029654 |
| 6.5 | 92 | 0.019078 |
| 4.6 | 75 | 0.015553 |
| 5.4 | 67 | 0.013894 |
| 0.5 | 55 | 0.011405 |
| 2.8 | 45 | 0.009332 |
| 3.3 | 31 | 0.006429 |
| 4.3 | 25 | 0.005184 |
| 1.8 | 19 | 0.003940 |
| 3.8 | 18 | 0.003733 |
| 1.6 | 17 | 0.003525 |
| 2.2 | 17 | 0.003525 |
| 1.9 | 15 | 0.003111 |
| 1.3 | 14 | 0.002903 |
| 1.4 | 12 | 0.002488 |
| 5.1 | 12 | 0.002488 |
| 0.0 | 9 | 0.001866 |
| 6.2 | 6 | 0.001244 |
| 2.3 | 6 | 0.001244 |
| 1.2 | 6 | 0.001244 |
| 4.8 | 4 | 0.000829 |
| 5.9 | 3 | 0.000622 |
| 0.9 | 3 | 0.000622 |
| 7.0 | 2 | 0.000415 |
| 5.6 | 2 | 0.000415 |
| 1.7 | 2 | 0.000415 |
| 6.1 | 1 | 0.000207 |
| 1.1 | 1 | 0.000207 |
| 6.7 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrvtotal__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 5.0 | 182563 | 37.858547 |
| 4.2 | 139953 | 29.022405 |
| 2.6 | 91942 | 19.066243 |
| 1.8 | 31583 | 6.549446 |
| 5.8 | 16834 | 3.490909 |
| 3.4 | 13939 | 2.890565 |
| 6.6 | 3844 | 0.797140 |
| 7.4 | 320 | 0.066359 |
| 3.2 | 307 | 0.063663 |
| 4.0 | 284 | 0.058894 |
| 1.6 | 281 | 0.058272 |
| 2.4 | 69 | 0.014309 |
| 0.8 | 55 | 0.011405 |
| 3.8 | 48 | 0.009954 |
| 8.2 | 36 | 0.007465 |
| 2.2 | 34 | 0.007051 |
| 3.6 | 34 | 0.007051 |
| 4.8 | 30 | 0.006221 |
| 4.4 | 19 | 0.003940 |
| 4.6 | 17 | 0.003525 |
| 2.0 | 12 | 0.002488 |
| 0.0 | 7 | 0.001452 |
| 1.2 | 5 | 0.001037 |
| 1.4 | 4 | 0.000829 |
| 3.0 | 3 | 0.000622 |
| 7.2 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_mailingsegment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_mailingsegment__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_mailingsegment__c es 7. Lo que supone un 0.0014516075516772288%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| SOC NO REC SIN EXTRA | 313670 | 65.046534 |
| SOC CON EXTRA ACT | 47969 | 9.947452 |
| SOC CON EXTRA NO REC | 38684 | 8.021998 |
| SOC NUEVOS | 29419 | 6.100692 |
| SOC CON EXTRA REC | 28788 | 5.969840 |
| SOC REC SIN EXTRA | 21232 | 4.402933 |
| EMPRESAS SOCIAS | 2381 | 0.493754 |
| No cumple ninguno de los criterios anteriores | 68 | 0.014101 |
| 7 | 0.001452 | |
| BAJAS ANTIGUAS | 2 | 0.000415 |
| BAJAS ACT | 2 | 0.000415 |
| DON PS ACT | 1 | 0.000207 |
| DON UNICO REC | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_membertype__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_membertype__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_membertype__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Socio | 301335 | 62.488595 |
| Socio + Exdonante | 132714 | 27.521235 |
| Socio + Donante | 48175 | 9.990171 |
# Vamos a realizar analisis por cada variable
var = "npo02__totaloppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 60.00 | 2118 | 0.439215 |
| 120.00 | 1991 | 0.412879 |
| 600.00 | 1773 | 0.367671 |
| 300.00 | 1769 | 0.366842 |
| 240.00 | 1660 | 0.344238 |
| ... | ... | ... |
| 3710.16 | 1 | 0.000207 |
| 2941.45 | 1 | 0.000207 |
| 426.21 | 1 | 0.000207 |
| 10741.52 | 1 | 0.000207 |
| 1628.70 | 1 | 0.000207 |
58885 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountthisyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 482224 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__oppamount2yearsago__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 482224 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountlastyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 482224 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year_total__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable npo02__best_gift_year_total__c es 825. Lo que supone un 0.17108231859053055% El nº de vacios para la variable npo02__best_gift_year_total__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.00 | 56389 | 11.713568 |
| 180.00 | 34896 | 7.248873 |
| 240.00 | 24252 | 5.037817 |
| 60.00 | 24010 | 4.987547 |
| 144.00 | 15062 | 3.128798 |
| ... | ... | ... |
| 8013.95 | 1 | 0.000208 |
| 4080.00 | 1 | 0.000208 |
| 569.00 | 1 | 0.000208 |
| 4074.00 | 1 | 0.000208 |
| 161.01 | 1 | 0.000208 |
5063 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_totalfiscaloppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_totalfiscaloppamount__c es 3. Lo que supone un 0.0006221175221473838% El nº de vacios para la variable msf_totalfiscaloppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 60.00 | 2118 | 0.439218 |
| 120.00 | 1990 | 0.412674 |
| 600.00 | 1773 | 0.367674 |
| 300.00 | 1769 | 0.366844 |
| 240.00 | 1660 | 0.344241 |
| ... | ... | ... |
| 4777.66 | 1 | 0.000207 |
| 10743.15 | 1 | 0.000207 |
| 1301.30 | 1 | 0.000207 |
| 5800.98 | 1 | 0.000207 |
| 1628.70 | 1 | 0.000207 |
58918 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lastannualizedquota__c es 1. Lo que supone un 0.0002073725073824613% El nº de vacios para la variable msf_lastannualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.00 | 86866 | 18.013658 |
| 180.00 | 52333 | 10.852448 |
| 240.00 | 38002 | 7.880586 |
| 60.00 | 37911 | 7.861715 |
| 144.00 | 26221 | 5.437526 |
| 300.00 | 16044 | 3.327091 |
| 72.00 | 15616 | 3.238336 |
| 360.00 | 15207 | 3.153520 |
| 168.00 | 10851 | 2.250204 |
| 96.00 | 10022 | 2.078292 |
| 84.00 | 9342 | 1.937278 |
| 100.00 | 9122 | 1.891656 |
| 36.00 | 8783 | 1.821357 |
| 204.00 | 5975 | 1.239053 |
| 200.00 | 5333 | 1.105920 |
| 600.00 | 5142 | 1.066312 |
| 50.00 | 5118 | 1.061335 |
| 480.00 | 5113 | 1.060298 |
| 80.00 | 4653 | 0.964906 |
| 216.00 | 4515 | 0.936289 |
| 150.00 | 4346 | 0.901243 |
| 192.00 | 4177 | 0.866197 |
| 420.00 | 4119 | 0.854169 |
| 156.00 | 3710 | 0.769354 |
| 30.00 | 3646 | 0.756082 |
| 108.00 | 3585 | 0.743432 |
| 20.00 | 3489 | 0.723524 |
| 312.00 | 3444 | 0.714192 |
| 40.00 | 3368 | 0.698432 |
| 132.00 | 3262 | 0.676451 |
| 264.00 | 3250 | 0.673962 |
| 228.00 | 2926 | 0.606773 |
| 72.12 | 2852 | 0.591428 |
| 160.00 | 2425 | 0.502879 |
| 276.00 | 2165 | 0.448962 |
| 720.00 | 1983 | 0.411221 |
| 12.00 | 1834 | 0.380322 |
| 48.00 | 1829 | 0.379285 |
| 140.00 | 1773 | 0.367672 |
| 90.00 | 1647 | 0.341543 |
| 10.00 | 1570 | 0.325576 |
| 70.00 | 1511 | 0.313341 |
| 51.96 | 1493 | 0.309608 |
| 15.00 | 1420 | 0.294470 |
| 384.00 | 1339 | 0.277672 |
| 400.00 | 1332 | 0.276221 |
| 540.00 | 1332 | 0.276221 |
| 60.10 | 1132 | 0.234746 |
| 1200.00 | 1131 | 0.234539 |
| 288.00 | 1115 | 0.231221 |
| 120.20 | 1099 | 0.227903 |
| 75.00 | 1097 | 0.227488 |
| 336.00 | 1014 | 0.210276 |
| 250.00 | 981 | 0.203433 |
| 324.00 | 978 | 0.202811 |
| 25.00 | 942 | 0.195345 |
| 252.00 | 855 | 0.177304 |
| 260.00 | 849 | 0.176060 |
| 30.05 | 809 | 0.167765 |
| 144.24 | 675 | 0.139977 |
| 396.00 | 638 | 0.132304 |
| 3.00 | 602 | 0.124839 |
| 130.00 | 601 | 0.124631 |
| 110.00 | 589 | 0.122143 |
| 360.60 | 576 | 0.119447 |
| 280.00 | 575 | 0.119239 |
| 24.00 | 547 | 0.113433 |
| 500.00 | 534 | 0.110737 |
| 220.00 | 529 | 0.109700 |
| 660.00 | 504 | 0.104516 |
| 840.00 | 498 | 0.103272 |
| 125.00 | 497 | 0.103064 |
| 320.00 | 481 | 0.099746 |
| 216.36 | 446 | 0.092488 |
| 5.00 | 436 | 0.090415 |
| 45.00 | 412 | 0.085438 |
| 35.00 | 407 | 0.084401 |
| 90.15 | 372 | 0.077143 |
| 170.00 | 336 | 0.069677 |
| 900.00 | 335 | 0.069470 |
| 65.00 | 335 | 0.069470 |
| 960.00 | 333 | 0.069055 |
| 240.40 | 331 | 0.068640 |
| 408.00 | 325 | 0.067396 |
| 432.00 | 307 | 0.063663 |
| 88.00 | 302 | 0.062627 |
| 780.00 | 290 | 0.060138 |
| 210.00 | 289 | 0.059931 |
| 18.00 | 279 | 0.057857 |
| 350.00 | 272 | 0.056405 |
| 444.00 | 262 | 0.054332 |
| 32.00 | 255 | 0.052880 |
| 1000.00 | 246 | 0.051014 |
| 175.00 | 244 | 0.050599 |
| 504.00 | 244 | 0.050599 |
| 55.00 | 240 | 0.049770 |
| 624.00 | 240 | 0.049770 |
| 372.00 | 238 | 0.049355 |
| 800.00 | 215 | 0.044585 |
| 165.00 | 212 | 0.043963 |
| 348.00 | 210 | 0.043548 |
| 230.00 | 207 | 0.042926 |
| 85.00 | 204 | 0.042304 |
| 42.00 | 200 | 0.041475 |
| 456.00 | 195 | 0.040438 |
| 18.03 | 193 | 0.040023 |
| 1080.00 | 190 | 0.039401 |
| 520.00 | 186 | 0.038571 |
| 52.00 | 181 | 0.037535 |
| 28.00 | 181 | 0.037535 |
| 22.00 | 177 | 0.036705 |
| 56.00 | 161 | 0.033387 |
| 112.00 | 158 | 0.032765 |
| 1800.00 | 148 | 0.030691 |
| 92.00 | 144 | 0.029862 |
| 105.00 | 140 | 0.029032 |
| 48.08 | 135 | 0.027995 |
| 340.00 | 127 | 0.026336 |
| 150.25 | 126 | 0.026129 |
| 152.00 | 126 | 0.026129 |
| 721.20 | 125 | 0.025922 |
| 516.00 | 122 | 0.025299 |
| 440.00 | 121 | 0.025092 |
| 528.00 | 121 | 0.025092 |
| 135.00 | 116 | 0.024055 |
| 68.00 | 115 | 0.023848 |
| 225.00 | 112 | 0.023226 |
| 6.00 | 112 | 0.023226 |
| 190.00 | 112 | 0.023226 |
| 104.00 | 111 | 0.023018 |
| 128.00 | 105 | 0.021774 |
| 2400.00 | 105 | 0.021774 |
| 224.00 | 104 | 0.021567 |
| 115.00 | 103 | 0.021359 |
| 64.00 | 102 | 0.021152 |
| 552.00 | 102 | 0.021152 |
| 124.00 | 96 | 0.019908 |
| 450.00 | 95 | 0.019700 |
| 270.00 | 94 | 0.019493 |
| 700.00 | 93 | 0.019286 |
| 62.00 | 90 | 0.018664 |
| 104.04 | 88 | 0.018249 |
| 148.00 | 86 | 0.017834 |
| 14.00 | 86 | 0.017834 |
| 864.00 | 86 | 0.017834 |
| 1440.00 | 85 | 0.017627 |
| 232.00 | 85 | 0.017627 |
| 492.00 | 84 | 0.017419 |
| 1500.00 | 83 | 0.017212 |
| 54.00 | 82 | 0.017005 |
| 95.00 | 81 | 0.016797 |
| 36.06 | 76 | 0.015760 |
| 380.00 | 76 | 0.015760 |
| 38.00 | 75 | 0.015553 |
| 1020.00 | 73 | 0.015138 |
| 16.00 | 72 | 0.014931 |
| 460.00 | 71 | 0.014723 |
| 108.12 | 70 | 0.014516 |
| 34.85 | 69 | 0.014309 |
| 468.00 | 68 | 0.014101 |
| 8.00 | 68 | 0.014101 |
| 184.00 | 67 | 0.013894 |
| 17.00 | 64 | 0.013272 |
| 76.00 | 63 | 0.013064 |
| 310.00 | 63 | 0.013064 |
| 24.04 | 62 | 0.012857 |
| 44.00 | 60 | 0.012442 |
| 66.00 | 59 | 0.012235 |
| 330.00 | 58 | 0.012028 |
| 164.00 | 58 | 0.012028 |
| 180.24 | 57 | 0.011820 |
| 180.30 | 56 | 0.011613 |
| 390.00 | 56 | 0.011613 |
| 155.00 | 55 | 0.011406 |
| 103.92 | 55 | 0.011406 |
| 136.00 | 55 | 0.011406 |
| 275.00 | 54 | 0.011198 |
| 576.00 | 53 | 0.010991 |
| 74.00 | 52 | 0.010783 |
| 96.16 | 52 | 0.010783 |
| 139.40 | 51 | 0.010576 |
| 21.00 | 51 | 0.010576 |
| 560.00 | 48 | 0.009954 |
| 1320.00 | 47 | 0.009747 |
| 300.50 | 45 | 0.009332 |
| 26.00 | 45 | 0.009332 |
| 288.48 | 43 | 0.008917 |
| 145.00 | 41 | 0.008502 |
| 550.00 | 41 | 0.008502 |
| 2000.00 | 40 | 0.008295 |
| 640.00 | 40 | 0.008295 |
| 208.00 | 40 | 0.008295 |
| 564.00 | 39 | 0.008088 |
| 116.00 | 38 | 0.007880 |
| 648.00 | 37 | 0.007673 |
| 33.00 | 37 | 0.007673 |
| 185.00 | 36 | 0.007465 |
| 27.00 | 36 | 0.007465 |
| 126.00 | 35 | 0.007258 |
| 212.00 | 35 | 0.007258 |
| 248.00 | 35 | 0.007258 |
| 93.15 | 34 | 0.007051 |
| 744.00 | 34 | 0.007051 |
| 620.00 | 34 | 0.007051 |
| 290.00 | 34 | 0.007051 |
| 172.00 | 33 | 0.006843 |
| 82.00 | 33 | 0.006843 |
| 364.00 | 33 | 0.006843 |
| 78.00 | 33 | 0.006843 |
| 63.00 | 32 | 0.006636 |
| 3000.00 | 32 | 0.006636 |
| 72.24 | 31 | 0.006429 |
| 188.00 | 30 | 0.006221 |
| 12.02 | 30 | 0.006221 |
| 102.00 | 30 | 0.006221 |
| 3600.00 | 29 | 0.006014 |
| 162.00 | 27 | 0.005599 |
| 176.00 | 26 | 0.005392 |
| 375.00 | 26 | 0.005392 |
| 196.00 | 26 | 0.005392 |
| 34.00 | 26 | 0.005392 |
| 370.00 | 26 | 0.005392 |
| 63.96 | 25 | 0.005184 |
| 1560.00 | 25 | 0.005184 |
| 650.00 | 24 | 0.004977 |
| 325.00 | 24 | 0.004977 |
| 792.00 | 24 | 0.004977 |
| 98.00 | 24 | 0.004977 |
| 612.00 | 24 | 0.004977 |
| 75.96 | 23 | 0.004770 |
| 7.00 | 22 | 0.004562 |
| 1008.00 | 22 | 0.004562 |
| 6.01 | 22 | 0.004562 |
| 601.00 | 21 | 0.004355 |
| 636.00 | 21 | 0.004355 |
| 173.04 | 21 | 0.004355 |
| 432.72 | 21 | 0.004355 |
| 46.00 | 21 | 0.004355 |
| 236.00 | 20 | 0.004147 |
| 168.28 | 20 | 0.004147 |
| 67.00 | 20 | 0.004147 |
| 392.00 | 19 | 0.003940 |
| 94.00 | 19 | 0.003940 |
| 192.32 | 19 | 0.003940 |
| 36.04 | 19 | 0.003940 |
| 235.00 | 19 | 0.003940 |
| 1081.80 | 19 | 0.003940 |
| 37.00 | 18 | 0.003733 |
| 365.00 | 18 | 0.003733 |
| 60.08 | 17 | 0.003525 |
| 174.00 | 17 | 0.003525 |
| 480.80 | 17 | 0.003525 |
| 39.00 | 16 | 0.003318 |
| 272.00 | 16 | 0.003318 |
| 750.00 | 16 | 0.003318 |
| 601.01 | 16 | 0.003318 |
| 1140.00 | 16 | 0.003318 |
| 13.00 | 16 | 0.003318 |
| 936.00 | 16 | 0.003318 |
| 215.00 | 15 | 0.003111 |
| 9.00 | 14 | 0.002903 |
| 760.00 | 14 | 0.002903 |
| 180.28 | 14 | 0.002903 |
| 77.00 | 14 | 0.002903 |
| 205.00 | 14 | 0.002903 |
| 31.00 | 14 | 0.002903 |
| 244.00 | 13 | 0.002696 |
| 11.00 | 13 | 0.002696 |
| 1680.00 | 13 | 0.002696 |
| 418.20 | 13 | 0.002696 |
| 6000.00 | 13 | 0.002696 |
| 4.00 | 13 | 0.002696 |
| 680.00 | 13 | 0.002696 |
| 425.00 | 13 | 0.002696 |
| 1600.00 | 13 | 0.002696 |
| 54.09 | 12 | 0.002488 |
| 1152.00 | 12 | 0.002488 |
| 304.00 | 12 | 0.002488 |
| 61.00 | 12 | 0.002488 |
| 1260.00 | 12 | 0.002488 |
| 684.00 | 12 | 0.002488 |
| 51.00 | 12 | 0.002488 |
| 672.00 | 12 | 0.002488 |
| 84.14 | 12 | 0.002488 |
| 576.96 | 11 | 0.002281 |
| 122.00 | 10 | 0.002074 |
| 361.44 | 10 | 0.002074 |
| 138.00 | 10 | 0.002074 |
| 28.84 | 10 | 0.002074 |
| 410.00 | 10 | 0.002074 |
| 86.00 | 10 | 0.002074 |
| 268.00 | 10 | 0.002074 |
| 23.00 | 10 | 0.002074 |
| 195.00 | 10 | 0.002074 |
| 865.44 | 10 | 0.002074 |
| 114.00 | 10 | 0.002074 |
| 108.18 | 10 | 0.002074 |
| 696.00 | 10 | 0.002074 |
| 1032.00 | 9 | 0.001866 |
| 87.00 | 9 | 0.001866 |
| 880.00 | 9 | 0.001866 |
| 768.00 | 9 | 0.001866 |
| 580.00 | 9 | 0.001866 |
| 284.00 | 9 | 0.001866 |
| 84.12 | 9 | 0.001866 |
| 78.13 | 9 | 0.001866 |
| 73.00 | 9 | 0.001866 |
| 756.00 | 9 | 0.001866 |
| 292.00 | 9 | 0.001866 |
| 344.00 | 8 | 0.001659 |
| 109.44 | 8 | 0.001659 |
| 316.00 | 8 | 0.001659 |
| 732.00 | 8 | 0.001659 |
| 81.00 | 8 | 0.001659 |
| 1442.40 | 8 | 0.001659 |
| 198.00 | 8 | 0.001659 |
| 2040.00 | 8 | 0.001659 |
| 134.00 | 8 | 0.001659 |
| 470.00 | 8 | 0.001659 |
| 2160.00 | 8 | 0.001659 |
| 42.07 | 8 | 0.001659 |
| 1100.00 | 8 | 0.001659 |
| 115.36 | 8 | 0.001659 |
| 1400.00 | 8 | 0.001659 |
| 504.84 | 7 | 0.001452 |
| 430.00 | 7 | 0.001452 |
| 41.00 | 7 | 0.001452 |
| 106.00 | 7 | 0.001452 |
| 4800.00 | 7 | 0.001452 |
| 101.00 | 7 | 0.001452 |
| 804.00 | 7 | 0.001452 |
| 255.00 | 7 | 0.001452 |
| 83.00 | 7 | 0.001452 |
| 47.00 | 7 | 0.001452 |
| 222.00 | 7 | 0.001452 |
| 142.00 | 7 | 0.001452 |
| 57.00 | 7 | 0.001452 |
| 820.00 | 7 | 0.001452 |
| 2100.00 | 7 | 0.001452 |
| 58.00 | 7 | 0.001452 |
| 256.00 | 7 | 0.001452 |
| 29.00 | 6 | 0.001244 |
| 372.60 | 6 | 0.001244 |
| 182.00 | 6 | 0.001244 |
| 888.00 | 6 | 0.001244 |
| 296.00 | 6 | 0.001244 |
| 120.12 | 6 | 0.001244 |
| 332.00 | 6 | 0.001244 |
| 920.00 | 6 | 0.001244 |
| 202.00 | 6 | 0.001244 |
| 376.00 | 6 | 0.001244 |
| 96.12 | 6 | 0.001244 |
| 850.00 | 6 | 0.001244 |
| 328.00 | 6 | 0.001244 |
| 53.00 | 6 | 0.001244 |
| 57.69 | 6 | 0.001244 |
| 60.24 | 6 | 0.001244 |
| 1380.00 | 6 | 0.001244 |
| 816.00 | 6 | 0.001244 |
| 285.00 | 6 | 0.001244 |
| 984.00 | 5 | 0.001037 |
| 368.00 | 5 | 0.001037 |
| 852.00 | 5 | 0.001037 |
| 93.00 | 5 | 0.001037 |
| 7200.00 | 5 | 0.001037 |
| 154.00 | 5 | 0.001037 |
| 186.00 | 5 | 0.001037 |
| 123.00 | 5 | 0.001037 |
| 252.36 | 5 | 0.001037 |
| 1620.00 | 5 | 0.001037 |
| 132.20 | 5 | 0.001037 |
| 120.48 | 5 | 0.001037 |
| 1040.00 | 5 | 0.001037 |
| 19.00 | 5 | 0.001037 |
| 305.00 | 5 | 0.001037 |
| 91.00 | 5 | 0.001037 |
| 416.00 | 5 | 0.001037 |
| 1920.00 | 5 | 0.001037 |
| 308.00 | 5 | 0.001037 |
| 43.00 | 5 | 0.001037 |
| 57.68 | 5 | 0.001037 |
| 588.00 | 5 | 0.001037 |
| 245.00 | 5 | 0.001037 |
| 1803.00 | 5 | 0.001037 |
| 97.00 | 5 | 0.001037 |
| 625.00 | 4 | 0.000829 |
| 475.00 | 4 | 0.000829 |
| 45.07 | 4 | 0.000829 |
| 912.00 | 4 | 0.000829 |
| 40.05 | 4 | 0.000829 |
| 14.42 | 4 | 0.000829 |
| 194.00 | 4 | 0.000829 |
| 448.00 | 4 | 0.000829 |
| 108.16 | 4 | 0.000829 |
| 286.00 | 4 | 0.000829 |
| 5000.00 | 4 | 0.000829 |
| 300.51 | 4 | 0.000829 |
| 1202.00 | 4 | 0.000829 |
| 161.00 | 4 | 0.000829 |
| 180.36 | 4 | 0.000829 |
| 346.08 | 4 | 0.000829 |
| 388.00 | 4 | 0.000829 |
| 90.36 | 4 | 0.000829 |
| 182.40 | 4 | 0.000829 |
| 201.00 | 4 | 0.000829 |
| 118.00 | 4 | 0.000829 |
| 345.00 | 4 | 0.000829 |
| 740.00 | 4 | 0.000829 |
| 90.12 | 4 | 0.000829 |
| 352.00 | 4 | 0.000829 |
| 210.35 | 4 | 0.000829 |
| 265.00 | 3 | 0.000622 |
| 12000.00 | 3 | 0.000622 |
| 396.60 | 3 | 0.000622 |
| 149.00 | 3 | 0.000622 |
| 876.00 | 3 | 0.000622 |
| 1128.00 | 3 | 0.000622 |
| 30.12 | 3 | 0.000622 |
| 66.11 | 3 | 0.000622 |
| 166.00 | 3 | 0.000622 |
| 488.00 | 3 | 0.000622 |
| 18000.00 | 3 | 0.000622 |
| 996.00 | 3 | 0.000622 |
| 43.20 | 3 | 0.000622 |
| 315.00 | 3 | 0.000622 |
| 1120.00 | 3 | 0.000622 |
| 708.00 | 3 | 0.000622 |
| 234.00 | 3 | 0.000622 |
| 450.75 | 3 | 0.000622 |
| 50.40 | 3 | 0.000622 |
| 100.15 | 3 | 0.000622 |
| 510.00 | 3 | 0.000622 |
| 274.00 | 3 | 0.000622 |
| 151.00 | 3 | 0.000622 |
| 262.00 | 3 | 0.000622 |
| 924.00 | 3 | 0.000622 |
| 240.36 | 3 | 0.000622 |
| 187.00 | 3 | 0.000622 |
| 71.00 | 3 | 0.000622 |
| 35.05 | 3 | 0.000622 |
| 424.00 | 3 | 0.000622 |
| 1300.00 | 3 | 0.000622 |
| 828.00 | 3 | 0.000622 |
| 8.66 | 2 | 0.000415 |
| 158.00 | 2 | 0.000415 |
| 137.00 | 2 | 0.000415 |
| 36.66 | 2 | 0.000415 |
| 93.24 | 2 | 0.000415 |
| 189.00 | 2 | 0.000415 |
| 3606.00 | 2 | 0.000415 |
| 109.00 | 2 | 0.000415 |
| 725.00 | 2 | 0.000415 |
| 570.00 | 2 | 0.000415 |
| 412.00 | 2 | 0.000415 |
| 530.00 | 2 | 0.000415 |
| 860.00 | 2 | 0.000415 |
| 65.10 | 2 | 0.000415 |
| 1596.00 | 2 | 0.000415 |
| 171.96 | 2 | 0.000415 |
| 121.00 | 2 | 0.000415 |
| 49.00 | 2 | 0.000415 |
| 1104.00 | 2 | 0.000415 |
| 153.00 | 2 | 0.000415 |
| 113.00 | 2 | 0.000415 |
| 1224.00 | 2 | 0.000415 |
| 89.00 | 2 | 0.000415 |
| 17.32 | 2 | 0.000415 |
| 223.92 | 2 | 0.000415 |
| 1360.00 | 2 | 0.000415 |
| 1860.00 | 2 | 0.000415 |
| 1464.00 | 2 | 0.000415 |
| 3900.00 | 2 | 0.000415 |
| 1056.00 | 2 | 0.000415 |
| 69.00 | 2 | 0.000415 |
| 30.04 | 2 | 0.000415 |
| 146.00 | 2 | 0.000415 |
| 159.96 | 2 | 0.000415 |
| 4000.00 | 2 | 0.000415 |
| 294.00 | 2 | 0.000415 |
| 356.00 | 2 | 0.000415 |
| 282.00 | 2 | 0.000415 |
| 60.05 | 2 | 0.000415 |
| 1980.00 | 2 | 0.000415 |
| 111.00 | 2 | 0.000415 |
| 79.32 | 2 | 0.000415 |
| 446.00 | 2 | 0.000415 |
| 159.40 | 2 | 0.000415 |
| 536.00 | 2 | 0.000415 |
| 9.01 | 2 | 0.000415 |
| 1284.00 | 2 | 0.000415 |
| 127.00 | 2 | 0.000415 |
| 92.12 | 2 | 0.000415 |
| 295.00 | 2 | 0.000415 |
| 420.60 | 2 | 0.000415 |
| 525.00 | 2 | 0.000415 |
| 70.10 | 2 | 0.000415 |
| 346.00 | 2 | 0.000415 |
| 2163.60 | 2 | 0.000415 |
| 103.00 | 2 | 0.000415 |
| 42.05 | 2 | 0.000415 |
| 99.00 | 2 | 0.000415 |
| 306.00 | 2 | 0.000415 |
| 14.40 | 2 | 0.000415 |
| 218.00 | 2 | 0.000415 |
| 540.60 | 2 | 0.000415 |
| 177.00 | 2 | 0.000415 |
| 810.00 | 2 | 0.000415 |
| 86.52 | 2 | 0.000415 |
| 167.00 | 2 | 0.000415 |
| 366.00 | 2 | 0.000415 |
| 585.00 | 2 | 0.000415 |
| 2500.00 | 2 | 0.000415 |
| 173.00 | 2 | 0.000415 |
| 157.00 | 2 | 0.000415 |
| 139.36 | 1 | 0.000207 |
| 390.15 | 1 | 0.000207 |
| 438.00 | 1 | 0.000207 |
| 4200.00 | 1 | 0.000207 |
| 10.50 | 1 | 0.000207 |
| 2328.00 | 1 | 0.000207 |
| 300.20 | 1 | 0.000207 |
| 188.04 | 1 | 0.000207 |
| 2200.00 | 1 | 0.000207 |
| 17.34 | 1 | 0.000207 |
| 100.10 | 1 | 0.000207 |
| 675.00 | 1 | 0.000207 |
| 532.00 | 1 | 0.000207 |
| 812.00 | 1 | 0.000207 |
| 710.00 | 1 | 0.000207 |
| 280.40 | 1 | 0.000207 |
| 1356.00 | 1 | 0.000207 |
| 132.12 | 1 | 0.000207 |
| 728.00 | 1 | 0.000207 |
| 824.56 | 1 | 0.000207 |
| 79.20 | 1 | 0.000207 |
| 1803.03 | 1 | 0.000207 |
| 1250.00 | 1 | 0.000207 |
| 210.32 | 1 | 0.000207 |
| 164.40 | 1 | 0.000207 |
| 214.00 | 1 | 0.000207 |
| 669.60 | 1 | 0.000207 |
| 80.40 | 1 | 0.000207 |
| 240.12 | 1 | 0.000207 |
| 436.00 | 1 | 0.000207 |
| 212.76 | 1 | 0.000207 |
| 480.24 | 1 | 0.000207 |
| 171.00 | 1 | 0.000207 |
| 216.72 | 1 | 0.000207 |
| 322.00 | 1 | 0.000207 |
| 842.88 | 1 | 0.000207 |
| 722.88 | 1 | 0.000207 |
| 796.00 | 1 | 0.000207 |
| 79.92 | 1 | 0.000207 |
| 2016.00 | 1 | 0.000207 |
| 87.96 | 1 | 0.000207 |
| 133.32 | 1 | 0.000207 |
| 343.92 | 1 | 0.000207 |
| 225.35 | 1 | 0.000207 |
| 139.92 | 1 | 0.000207 |
| 206.00 | 1 | 0.000207 |
| 243.96 | 1 | 0.000207 |
| 30.40 | 1 | 0.000207 |
| 364.80 | 1 | 0.000207 |
| 2520.00 | 1 | 0.000207 |
| 180.10 | 1 | 0.000207 |
| 1644.00 | 1 | 0.000207 |
| 1512.00 | 1 | 0.000207 |
| 60.12 | 1 | 0.000207 |
| 260.10 | 1 | 0.000207 |
| 247.68 | 1 | 0.000207 |
| 120.10 | 1 | 0.000207 |
| 90.14 | 1 | 0.000207 |
| 973.20 | 1 | 0.000207 |
| 338.00 | 1 | 0.000207 |
| 592.00 | 1 | 0.000207 |
| 258.00 | 1 | 0.000207 |
| 1110.00 | 1 | 0.000207 |
| 1117.80 | 1 | 0.000207 |
| 1202.04 | 1 | 0.000207 |
| 138.15 | 1 | 0.000207 |
| 38.40 | 1 | 0.000207 |
| 462.00 | 1 | 0.000207 |
| 147.00 | 1 | 0.000207 |
| 12.50 | 1 | 0.000207 |
| 60.15 | 1 | 0.000207 |
| 558.00 | 1 | 0.000207 |
| 36.08 | 1 | 0.000207 |
| 2800.00 | 1 | 0.000207 |
| 14.02 | 1 | 0.000207 |
| 143.00 | 1 | 0.000207 |
| 123.96 | 1 | 0.000207 |
| 208.08 | 1 | 0.000207 |
| 968.00 | 1 | 0.000207 |
| 4080.00 | 1 | 0.000207 |
| 318.00 | 1 | 0.000207 |
| 323.00 | 1 | 0.000207 |
| 374.00 | 1 | 0.000207 |
| 3020.00 | 1 | 0.000207 |
| 333.00 | 1 | 0.000207 |
| 311.00 | 1 | 0.000207 |
| 272.12 | 1 | 0.000207 |
| 2640.00 | 1 | 0.000207 |
| 302.88 | 1 | 0.000207 |
| 60.01 | 1 | 0.000207 |
| 126.84 | 1 | 0.000207 |
| 1092.00 | 1 | 0.000207 |
| 484.00 | 1 | 0.000207 |
| 36.05 | 1 | 0.000207 |
| 152.24 | 1 | 0.000207 |
| 550.75 | 1 | 0.000207 |
| 132.22 | 1 | 0.000207 |
| 4500.00 | 1 | 0.000207 |
| 385.00 | 1 | 0.000207 |
| 150.15 | 1 | 0.000207 |
| 798.00 | 1 | 0.000207 |
| 281.00 | 1 | 0.000207 |
| 238.00 | 1 | 0.000207 |
| 505.00 | 1 | 0.000207 |
| 147.12 | 1 | 0.000207 |
| 224.24 | 1 | 0.000207 |
| 156.24 | 1 | 0.000207 |
| 240.20 | 1 | 0.000207 |
| 70.01 | 1 | 0.000207 |
| 260.40 | 1 | 0.000207 |
| 2884.80 | 1 | 0.000207 |
| 105.10 | 1 | 0.000207 |
| 409.44 | 1 | 0.000207 |
| 264.24 | 1 | 0.000207 |
| 90.75 | 1 | 0.000207 |
| 362.00 | 1 | 0.000207 |
| 211.52 | 1 | 0.000207 |
| 252.40 | 1 | 0.000207 |
| 138.23 | 1 | 0.000207 |
| 3800.00 | 1 | 0.000207 |
| 336.56 | 1 | 0.000207 |
| 402.00 | 1 | 0.000207 |
| 131.00 | 1 | 0.000207 |
| 1202.02 | 1 | 0.000207 |
| 100.92 | 1 | 0.000207 |
| 32.05 | 1 | 0.000207 |
| 692.28 | 1 | 0.000207 |
| 40.06 | 1 | 0.000207 |
| 415.00 | 1 | 0.000207 |
| 175.25 | 1 | 0.000207 |
| 466.64 | 1 | 0.000207 |
| 6600.00 | 1 | 0.000207 |
| 79.00 | 1 | 0.000207 |
| 641.00 | 1 | 0.000207 |
| 245.16 | 1 | 0.000207 |
| 270.03 | 1 | 0.000207 |
| 565.00 | 1 | 0.000207 |
| 901.00 | 1 | 0.000207 |
| 168.24 | 1 | 0.000207 |
| 65.86 | 1 | 0.000207 |
| 93.72 | 1 | 0.000207 |
| 1442.43 | 1 | 0.000207 |
| 242.00 | 1 | 0.000207 |
| 841.40 | 1 | 0.000207 |
| 302.00 | 1 | 0.000207 |
| 290.10 | 1 | 0.000207 |
| 1204.00 | 1 | 0.000207 |
| 193.00 | 1 | 0.000207 |
| 59.00 | 1 | 0.000207 |
| 102.17 | 1 | 0.000207 |
| 162.05 | 1 | 0.000207 |
| 126.15 | 1 | 0.000207 |
| 108.24 | 1 | 0.000207 |
| 110.40 | 1 | 0.000207 |
| 3606.12 | 1 | 0.000207 |
| 300.48 | 1 | 0.000207 |
| 1502.53 | 1 | 0.000207 |
| 55.92 | 1 | 0.000207 |
| 2880.00 | 1 | 0.000207 |
| 544.00 | 1 | 0.000207 |
| 144.20 | 1 | 0.000207 |
| 1752.00 | 1 | 0.000207 |
| 71.96 | 1 | 0.000207 |
| 270.05 | 1 | 0.000207 |
| 901.20 | 1 | 0.000207 |
| 273.00 | 1 | 0.000207 |
| 340.08 | 1 | 0.000207 |
| 458.00 | 1 | 0.000207 |
| 90.10 | 1 | 0.000207 |
| 1750.00 | 1 | 0.000207 |
| 320.50 | 1 | 0.000207 |
| 19.50 | 1 | 0.000207 |
| 199.00 | 1 | 0.000207 |
| 278.00 | 1 | 0.000207 |
| 77.10 | 1 | 0.000207 |
| 40.20 | 1 | 0.000207 |
| 164.24 | 1 | 0.000207 |
| 148.24 | 1 | 0.000207 |
| 608.00 | 1 | 0.000207 |
| 117.00 | 1 | 0.000207 |
| 169.00 | 1 | 0.000207 |
| 160.20 | 1 | 0.000207 |
| 382.08 | 1 | 0.000207 |
| 59.02 | 1 | 0.000207 |
| 181.00 | 1 | 0.000207 |
| 289.00 | 1 | 0.000207 |
| 382.00 | 1 | 0.000207 |
| 590.00 | 1 | 0.000207 |
| 2280.00 | 1 | 0.000207 |
| 335.00 | 1 | 0.000207 |
| 2700.00 | 1 | 0.000207 |
| 3360.00 | 1 | 0.000207 |
| 1908.00 | 1 | 0.000207 |
| 572.00 | 1 | 0.000207 |
| 241.00 | 1 | 0.000207 |
| 59.88 | 1 | 0.000207 |
| 3200.00 | 1 | 0.000207 |
| 107.00 | 1 | 0.000207 |
| 428.00 | 1 | 0.000207 |
| 76.12 | 1 | 0.000207 |
| 1160.00 | 1 | 0.000207 |
| 198.36 | 1 | 0.000207 |
| 253.00 | 1 | 0.000207 |
| 972.00 | 1 | 0.000207 |
| 39.96 | 1 | 0.000207 |
| 720.80 | 1 | 0.000207 |
| 4201.00 | 1 | 0.000207 |
| 99.60 | 1 | 0.000207 |
| 1992.00 | 1 | 0.000207 |
| 101.96 | 1 | 0.000207 |
| 99.96 | 1 | 0.000207 |
| 2760.00 | 1 | 0.000207 |
| 793.32 | 1 | 0.000207 |
| 160.25 | 1 | 0.000207 |
| 63.60 | 1 | 0.000207 |
| 140.20 | 1 | 0.000207 |
| 8000.00 | 1 | 0.000207 |
| 1502.40 | 1 | 0.000207 |
| 35.88 | 1 | 0.000207 |
| 1584.00 | 1 | 0.000207 |
| 158.64 | 1 | 0.000207 |
| 112.12 | 1 | 0.000207 |
| 55.56 | 1 | 0.000207 |
| 3180.00 | 1 | 0.000207 |
| 115.32 | 1 | 0.000207 |
| 240.10 | 1 | 0.000207 |
| 84.60 | 1 | 0.000207 |
| 480.60 | 1 | 0.000207 |
| 306.51 | 1 | 0.000207 |
| 468.12 | 1 | 0.000207 |
| 200.04 | 1 | 0.000207 |
| 4360.00 | 1 | 0.000207 |
| 75.72 | 1 | 0.000207 |
| 207.00 | 1 | 0.000207 |
| 741.00 | 1 | 0.000207 |
| 176.24 | 1 | 0.000207 |
| 3300.00 | 1 | 0.000207 |
| 199.20 | 1 | 0.000207 |
| 1044.00 | 1 | 0.000207 |
| 249.96 | 1 | 0.000207 |
| 277.00 | 1 | 0.000207 |
| 1280.00 | 1 | 0.000207 |
| 630.00 | 1 | 0.000207 |
| 451.00 | 1 | 0.000207 |
| 64.90 | 1 | 0.000207 |
| 464.00 | 1 | 0.000207 |
| 1116.00 | 1 | 0.000207 |
| 8400.00 | 1 | 0.000207 |
# Vamos a realizar analisis por cada variable
var = "msf_valuetotalcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_valuetotalcont__c es 7. Lo que supone un 0.0014516075516772288% El nº de vacios para la variable msf_valuetotalcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.0 | 81612 | 16.924331 |
| 180.0 | 48292 | 10.014578 |
| 60.0 | 37037 | 7.680567 |
| 240.0 | 34408 | 7.135377 |
| 144.0 | 24709 | 5.124042 |
| ... | ... | ... |
| 2670.0 | 1 | 0.000207 |
| 972.0 | 1 | 0.000207 |
| 921.0 | 1 | 0.000207 |
| 2170.0 | 1 | 0.000207 |
| 8400.0 | 1 | 0.000207 |
1493 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_valuetotaldesc__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_valuetotaldesc__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_valuetotaldesc__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Medio | 182862 | 37.920551 |
| Bajo | 140387 | 29.112404 |
| Muy bajo | 137923 | 28.601438 |
| Alto | 20688 | 4.290122 |
| Muy Alto | 357 | 0.074032 |
| Nulo | 7 | 0.001452 |
# Vamos a realizar analisis por cada variable
var = "msf_valuedonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_valuedonorcont__c es 301547. Lo que supone un 62.53255748365905% El nº de vacios para la variable msf_valuedonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 30.00 | 18977 | 10.503274 |
| 50.00 | 17792 | 9.847407 |
| 100.00 | 17790 | 9.846300 |
| 60.00 | 17422 | 9.642622 |
| 20.00 | 16709 | 9.247995 |
| ... | ... | ... |
| 365.12 | 1 | 0.000553 |
| 283.05 | 1 | 0.000553 |
| 1578.00 | 1 | 0.000553 |
| 321.00 | 1 | 0.000553 |
| 1.17 | 1 | 0.000553 |
1613 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastyeardonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lastyeardonorvalue__c es 434203. Lo que supone un 90.04176482298682% El nº de vacios para la variable msf_lastyeardonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 100.00 | 5218 | 10.866079 |
| 50.00 | 5073 | 10.564128 |
| 20.00 | 4290 | 8.933592 |
| 30.00 | 3918 | 8.158930 |
| 60.00 | 3858 | 8.033985 |
| 10.00 | 2052 | 4.273131 |
| 200.00 | 1927 | 4.012828 |
| 40.00 | 1873 | 3.900377 |
| 1.00 | 1837 | 3.825410 |
| 150.00 | 1261 | 2.625934 |
| 300.00 | 1024 | 2.132400 |
| 120.00 | 960 | 1.999125 |
| 90.00 | 940 | 1.957477 |
| 25.00 | 852 | 1.774224 |
| 80.00 | 773 | 1.609712 |
| 15.00 | 711 | 1.480602 |
| 5.00 | 662 | 1.378564 |
| 250.00 | 598 | 1.245289 |
| 500.00 | 564 | 1.174486 |
| 70.00 | 403 | 0.839216 |
| 400.00 | 399 | 0.830886 |
| 125.00 | 392 | 0.816310 |
| 2.00 | 295 | 0.614315 |
| 1000.00 | 279 | 0.580996 |
| 110.00 | 242 | 0.503946 |
| 160.00 | 236 | 0.491452 |
| 600.00 | 231 | 0.481040 |
| 180.00 | 229 | 0.476875 |
| 45.00 | 224 | 0.466463 |
| 140.00 | 183 | 0.381083 |
| 130.00 | 171 | 0.356094 |
| 350.00 | 163 | 0.339435 |
| 39.00 | 160 | 0.333188 |
| 75.00 | 156 | 0.324858 |
| 66.00 | 140 | 0.291539 |
| 240.00 | 139 | 0.289457 |
| 450.00 | 127 | 0.264468 |
| 3.00 | 125 | 0.260303 |
| 55.00 | 101 | 0.210325 |
| 220.00 | 101 | 0.210325 |
| 16.00 | 101 | 0.210325 |
| 35.00 | 99 | 0.206160 |
| 170.00 | 94 | 0.195748 |
| 2000.00 | 93 | 0.193665 |
| 800.00 | 85 | 0.177006 |
| 190.00 | 83 | 0.172841 |
| 78.00 | 82 | 0.170759 |
| 175.00 | 81 | 0.168676 |
| 12.00 | 81 | 0.168676 |
| 26.00 | 80 | 0.166594 |
| 61.00 | 77 | 0.160347 |
| 270.00 | 75 | 0.156182 |
| 210.00 | 74 | 0.154099 |
| 700.00 | 71 | 0.147852 |
| 4.00 | 64 | 0.133275 |
| 550.00 | 64 | 0.133275 |
| 8.00 | 61 | 0.127028 |
| 51.00 | 61 | 0.127028 |
| 1500.00 | 60 | 0.124945 |
| 6.00 | 58 | 0.120780 |
| 900.00 | 57 | 0.118698 |
| 750.00 | 57 | 0.118698 |
| 65.00 | 57 | 0.118698 |
| 225.00 | 55 | 0.114533 |
| 260.00 | 54 | 0.112451 |
| 3000.00 | 50 | 0.104121 |
| 21.00 | 50 | 0.104121 |
| 156.00 | 48 | 0.099956 |
| 24.00 | 48 | 0.099956 |
| 325.00 | 47 | 0.097874 |
| 101.00 | 47 | 0.097874 |
| 650.00 | 45 | 0.093709 |
| 31.00 | 45 | 0.093709 |
| 1200.00 | 42 | 0.087462 |
| 360.00 | 42 | 0.087462 |
| 230.00 | 41 | 0.085379 |
| 99.00 | 39 | 0.081214 |
| 280.00 | 36 | 0.074967 |
| 320.00 | 36 | 0.074967 |
| 85.00 | 36 | 0.074967 |
| 32.00 | 33 | 0.068720 |
| 7.00 | 28 | 0.058308 |
| 375.00 | 27 | 0.056225 |
| 340.00 | 27 | 0.056225 |
| 275.00 | 27 | 0.056225 |
| 105.00 | 27 | 0.056225 |
| 1100.00 | 25 | 0.052061 |
| 290.00 | 25 | 0.052061 |
| 69.00 | 25 | 0.052061 |
| 41.00 | 25 | 0.052061 |
| 185.00 | 24 | 0.049978 |
| 370.00 | 23 | 0.047896 |
| 1300.00 | 23 | 0.047896 |
| 11.00 | 23 | 0.047896 |
| 205.00 | 22 | 0.045813 |
| 850.00 | 22 | 0.045813 |
| 46.00 | 22 | 0.045813 |
| 34.00 | 22 | 0.045813 |
| 36.00 | 21 | 0.043731 |
| 14.00 | 21 | 0.043731 |
| 91.00 | 21 | 0.043731 |
| 1400.00 | 21 | 0.043731 |
| 138.00 | 21 | 0.043731 |
| 245.00 | 20 | 0.041648 |
| 2500.00 | 19 | 0.039566 |
| 4000.00 | 19 | 0.039566 |
| 425.00 | 18 | 0.037484 |
| 310.00 | 18 | 0.037484 |
| 52.00 | 18 | 0.037484 |
| 126.00 | 18 | 0.037484 |
| 151.00 | 18 | 0.037484 |
| 102.00 | 18 | 0.037484 |
| 18.00 | 18 | 0.037484 |
| 89.00 | 18 | 0.037484 |
| 420.00 | 17 | 0.035401 |
| 95.00 | 17 | 0.035401 |
| 115.00 | 17 | 0.035401 |
| 330.00 | 17 | 0.035401 |
| 390.00 | 16 | 0.033319 |
| 675.00 | 16 | 0.033319 |
| 62.00 | 16 | 0.033319 |
| 111.00 | 16 | 0.033319 |
| 56.00 | 15 | 0.031236 |
| 5000.00 | 15 | 0.031236 |
| 121.00 | 15 | 0.031236 |
| 76.00 | 15 | 0.031236 |
| 525.00 | 15 | 0.031236 |
| 128.00 | 14 | 0.029154 |
| 81.00 | 14 | 0.029154 |
| 86.00 | 13 | 0.027071 |
| 178.00 | 13 | 0.027071 |
| 950.00 | 13 | 0.027071 |
| 1600.00 | 13 | 0.027071 |
| 145.00 | 13 | 0.027071 |
| 165.00 | 13 | 0.027071 |
| 1250.00 | 13 | 0.027071 |
| 48.00 | 12 | 0.024989 |
| 6000.00 | 12 | 0.024989 |
| 1050.00 | 12 | 0.024989 |
| 117.00 | 12 | 0.024989 |
| 42.00 | 12 | 0.024989 |
| 295.00 | 11 | 0.022907 |
| 10000.00 | 11 | 0.022907 |
| 410.00 | 11 | 0.022907 |
| 475.00 | 11 | 0.022907 |
| 195.00 | 11 | 0.022907 |
| 520.00 | 11 | 0.022907 |
| 380.00 | 11 | 0.022907 |
| 64.00 | 11 | 0.022907 |
| 1450.00 | 11 | 0.022907 |
| 135.00 | 10 | 0.020824 |
| 116.00 | 10 | 0.020824 |
| 273.00 | 10 | 0.020824 |
| 72.00 | 10 | 0.020824 |
| 92.00 | 10 | 0.020824 |
| 560.00 | 10 | 0.020824 |
| 480.00 | 10 | 0.020824 |
| 22.00 | 10 | 0.020824 |
| 306.00 | 10 | 0.020824 |
| 9.00 | 10 | 0.020824 |
| 27.00 | 10 | 0.020824 |
| 490.00 | 9 | 0.018742 |
| 96.00 | 9 | 0.018742 |
| 158.00 | 9 | 0.018742 |
| 63.00 | 9 | 0.018742 |
| 155.00 | 9 | 0.018742 |
| 148.00 | 9 | 0.018742 |
| 440.00 | 9 | 0.018742 |
| 28.00 | 9 | 0.018742 |
| 625.00 | 9 | 0.018742 |
| 139.00 | 9 | 0.018742 |
| 59.00 | 9 | 0.018742 |
| 201.00 | 9 | 0.018742 |
| 198.00 | 9 | 0.018742 |
| 79.00 | 8 | 0.016659 |
| 301.00 | 8 | 0.016659 |
| 131.00 | 8 | 0.016659 |
| 305.00 | 8 | 0.016659 |
| 256.00 | 8 | 0.016659 |
| 211.00 | 8 | 0.016659 |
| 456.00 | 8 | 0.016659 |
| 33.00 | 8 | 0.016659 |
| 215.00 | 8 | 0.016659 |
| 430.00 | 8 | 0.016659 |
| 315.00 | 8 | 0.016659 |
| 285.00 | 8 | 0.016659 |
| 119.00 | 8 | 0.016659 |
| 281.00 | 8 | 0.016659 |
| 2400.00 | 8 | 0.016659 |
| 129.00 | 8 | 0.016659 |
| 356.00 | 7 | 0.014577 |
| 575.00 | 7 | 0.014577 |
| 166.00 | 7 | 0.014577 |
| 118.00 | 7 | 0.014577 |
| 23.00 | 7 | 0.014577 |
| 239.00 | 7 | 0.014577 |
| 217.00 | 7 | 0.014577 |
| 501.00 | 7 | 0.014577 |
| 53.00 | 7 | 0.014577 |
| 142.00 | 7 | 0.014577 |
| 725.00 | 7 | 0.014577 |
| 1800.00 | 7 | 0.014577 |
| 255.00 | 7 | 0.014577 |
| 17.00 | 6 | 0.012495 |
| 47.00 | 6 | 0.012495 |
| 415.00 | 6 | 0.012495 |
| 202.00 | 6 | 0.012495 |
| 775.00 | 6 | 0.012495 |
| 124.00 | 6 | 0.012495 |
| 104.00 | 6 | 0.012495 |
| 1700.00 | 6 | 0.012495 |
| 216.00 | 6 | 0.012495 |
| 2100.00 | 6 | 0.012495 |
| 84.00 | 6 | 0.012495 |
| 68.00 | 6 | 0.012495 |
| 161.00 | 6 | 0.012495 |
| 203.00 | 6 | 0.012495 |
| 470.00 | 6 | 0.012495 |
| 365.00 | 6 | 0.012495 |
| 168.00 | 5 | 0.010412 |
| 2800.00 | 5 | 0.010412 |
| 82.00 | 5 | 0.010412 |
| 510.00 | 5 | 0.010412 |
| 188.00 | 5 | 0.010412 |
| 2700.00 | 5 | 0.010412 |
| 620.00 | 5 | 0.010412 |
| 38.00 | 5 | 0.010412 |
| 1350.00 | 5 | 0.010412 |
| 264.00 | 5 | 0.010412 |
| 374.00 | 5 | 0.010412 |
| 73.00 | 5 | 0.010412 |
| 136.00 | 5 | 0.010412 |
| 169.00 | 5 | 0.010412 |
| 181.00 | 5 | 0.010412 |
| 460.00 | 5 | 0.010412 |
| 108.00 | 5 | 0.010412 |
| 171.00 | 5 | 0.010412 |
| 74.00 | 5 | 0.010412 |
| 395.00 | 5 | 0.010412 |
| 3500.00 | 5 | 0.010412 |
| 925.00 | 5 | 0.010412 |
| 141.00 | 5 | 0.010412 |
| 132.00 | 5 | 0.010412 |
| 825.00 | 5 | 0.010412 |
| 326.00 | 5 | 0.010412 |
| 199.00 | 5 | 0.010412 |
| 406.00 | 5 | 0.010412 |
| 71.00 | 5 | 0.010412 |
| 246.00 | 4 | 0.008330 |
| 206.00 | 4 | 0.008330 |
| 1750.00 | 4 | 0.008330 |
| 67.00 | 4 | 0.008330 |
| 606.00 | 4 | 0.008330 |
| 690.00 | 4 | 0.008330 |
| 351.00 | 4 | 0.008330 |
| 94.00 | 4 | 0.008330 |
| 1140.00 | 4 | 0.008330 |
| 258.00 | 4 | 0.008330 |
| 3200.00 | 4 | 0.008330 |
| 345.00 | 4 | 0.008330 |
| 465.00 | 4 | 0.008330 |
| 610.00 | 4 | 0.008330 |
| 54.00 | 4 | 0.008330 |
| 1150.00 | 4 | 0.008330 |
| 77.00 | 4 | 0.008330 |
| 20000.00 | 4 | 0.008330 |
| 189.00 | 4 | 0.008330 |
| 401.00 | 4 | 0.008330 |
| 7500.00 | 4 | 0.008330 |
| 820.00 | 4 | 0.008330 |
| 353.00 | 4 | 0.008330 |
| 265.00 | 4 | 0.008330 |
| 147.00 | 4 | 0.008330 |
| 106.00 | 4 | 0.008330 |
| 580.00 | 4 | 0.008330 |
| 149.00 | 4 | 0.008330 |
| 98.00 | 4 | 0.008330 |
| 177.00 | 4 | 0.008330 |
| 299.00 | 4 | 0.008330 |
| 7000.00 | 4 | 0.008330 |
| 109.00 | 4 | 0.008330 |
| 176.00 | 4 | 0.008330 |
| 19.00 | 4 | 0.008330 |
| 9000.00 | 3 | 0.006247 |
| 1325.00 | 3 | 0.006247 |
| 231.00 | 3 | 0.006247 |
| 399.00 | 3 | 0.006247 |
| 489.00 | 3 | 0.006247 |
| 278.00 | 3 | 0.006247 |
| 3400.00 | 3 | 0.006247 |
| 87.00 | 3 | 0.006247 |
| 376.00 | 3 | 0.006247 |
| 373.00 | 3 | 0.006247 |
| 122.00 | 3 | 0.006247 |
| 2300.00 | 3 | 0.006247 |
| 123.00 | 3 | 0.006247 |
| 302.00 | 3 | 0.006247 |
| 975.00 | 3 | 0.006247 |
| 271.00 | 3 | 0.006247 |
| 473.00 | 3 | 0.006247 |
| 235.00 | 3 | 0.006247 |
| 530.00 | 3 | 0.006247 |
| 524.00 | 3 | 0.006247 |
| 134.00 | 3 | 0.006247 |
| 445.00 | 3 | 0.006247 |
| 570.00 | 3 | 0.006247 |
| 539.00 | 3 | 0.006247 |
| 13.00 | 3 | 0.006247 |
| 630.00 | 3 | 0.006247 |
| 2200.00 | 3 | 0.006247 |
| 297.00 | 3 | 0.006247 |
| 468.00 | 3 | 0.006247 |
| 505.00 | 3 | 0.006247 |
| 107.00 | 3 | 0.006247 |
| 112.00 | 3 | 0.006247 |
| 506.00 | 3 | 0.006247 |
| 540.00 | 3 | 0.006247 |
| 780.00 | 3 | 0.006247 |
| 197.00 | 3 | 0.006247 |
| 361.00 | 3 | 0.006247 |
| 249.00 | 3 | 0.006247 |
| 8000.00 | 3 | 0.006247 |
| 143.00 | 3 | 0.006247 |
| 204.00 | 3 | 0.006247 |
| 221.00 | 3 | 0.006247 |
| 251.00 | 3 | 0.006247 |
| 37.00 | 3 | 0.006247 |
| 334.00 | 3 | 0.006247 |
| 615.00 | 3 | 0.006247 |
| 186.00 | 3 | 0.006247 |
| 146.00 | 3 | 0.006247 |
| 556.00 | 3 | 0.006247 |
| 660.00 | 3 | 0.006247 |
| 381.00 | 3 | 0.006247 |
| 710.00 | 2 | 0.004165 |
| 11000.00 | 2 | 0.004165 |
| 219.00 | 2 | 0.004165 |
| 595.00 | 2 | 0.004165 |
| 573.00 | 2 | 0.004165 |
| 2750.00 | 2 | 0.004165 |
| 687.00 | 2 | 0.004165 |
| 5250.00 | 2 | 0.004165 |
| 276.00 | 2 | 0.004165 |
| 1460.00 | 2 | 0.004165 |
| 152.00 | 2 | 0.004165 |
| 1580.00 | 2 | 0.004165 |
| 226.00 | 2 | 0.004165 |
| 429.00 | 2 | 0.004165 |
| 238.00 | 2 | 0.004165 |
| 448.00 | 2 | 0.004165 |
| 331.00 | 2 | 0.004165 |
| 1175.00 | 2 | 0.004165 |
| 3650.00 | 2 | 0.004165 |
| 1650.00 | 2 | 0.004165 |
| 269.00 | 2 | 0.004165 |
| 5400.00 | 2 | 0.004165 |
| 93.00 | 2 | 0.004165 |
| 1025.00 | 2 | 0.004165 |
| 222.00 | 2 | 0.004165 |
| 244.00 | 2 | 0.004165 |
| 252.00 | 2 | 0.004165 |
| 57.00 | 2 | 0.004165 |
| 44.00 | 2 | 0.004165 |
| 324.00 | 2 | 0.004165 |
| 495.00 | 2 | 0.004165 |
| 58.00 | 2 | 0.004165 |
| 99.99 | 2 | 0.004165 |
| 153.00 | 2 | 0.004165 |
| 348.00 | 2 | 0.004165 |
| 496.00 | 2 | 0.004165 |
| 159.00 | 2 | 0.004165 |
| 241.00 | 2 | 0.004165 |
| 1550.00 | 2 | 0.004165 |
| 756.00 | 2 | 0.004165 |
| 670.00 | 2 | 0.004165 |
| 378.00 | 2 | 0.004165 |
| 4500.00 | 2 | 0.004165 |
| 3100.00 | 2 | 0.004165 |
| 173.00 | 2 | 0.004165 |
| 25000.00 | 2 | 0.004165 |
| 262.00 | 2 | 0.004165 |
| 564.00 | 2 | 0.004165 |
| 1003.00 | 2 | 0.004165 |
| 261.00 | 2 | 0.004165 |
| 1075.00 | 2 | 0.004165 |
| 920.00 | 2 | 0.004165 |
| 316.00 | 2 | 0.004165 |
| 2600.00 | 2 | 0.004165 |
| 402.00 | 2 | 0.004165 |
| 208.00 | 2 | 0.004165 |
| 444.00 | 2 | 0.004165 |
| 354.00 | 2 | 0.004165 |
| 2900.00 | 2 | 0.004165 |
| 162.00 | 2 | 0.004165 |
| 323.00 | 2 | 0.004165 |
| 30.05 | 2 | 0.004165 |
| 223.00 | 2 | 0.004165 |
| 689.00 | 2 | 0.004165 |
| 263.00 | 2 | 0.004165 |
| 1968.00 | 2 | 0.004165 |
| 455.00 | 2 | 0.004165 |
| 43.00 | 2 | 0.004165 |
| 631.00 | 2 | 0.004165 |
| 590.00 | 2 | 0.004165 |
| 770.00 | 2 | 0.004165 |
| 218.00 | 2 | 0.004165 |
| 640.00 | 2 | 0.004165 |
| 114.00 | 2 | 0.004165 |
| 307.00 | 2 | 0.004165 |
| 49.00 | 2 | 0.004165 |
| 144.00 | 2 | 0.004165 |
| 103.00 | 2 | 0.004165 |
| 6500.00 | 2 | 0.004165 |
| 207.00 | 2 | 0.004165 |
| 12000.00 | 2 | 0.004165 |
| 790.00 | 2 | 0.004165 |
| 875.00 | 2 | 0.004165 |
| 1260.00 | 2 | 0.004165 |
| 565.00 | 2 | 0.004165 |
| 514.00 | 2 | 0.004165 |
| 234.00 | 2 | 0.004165 |
| 97.00 | 2 | 0.004165 |
| 1020.00 | 2 | 0.004165 |
| 338.00 | 2 | 0.004165 |
| 3600.00 | 2 | 0.004165 |
| 133.00 | 2 | 0.004165 |
| 266.00 | 2 | 0.004165 |
| 1950.00 | 2 | 0.004165 |
| 840.00 | 2 | 0.004165 |
| 16000.00 | 2 | 0.004165 |
| 1900.00 | 2 | 0.004165 |
| 187.00 | 2 | 0.004165 |
| 337.00 | 2 | 0.004165 |
| 1125.00 | 2 | 0.004165 |
| 1056.00 | 1 | 0.002082 |
| 346.00 | 1 | 0.002082 |
| 662.00 | 1 | 0.002082 |
| 502.00 | 1 | 0.002082 |
| 416.00 | 1 | 0.002082 |
| 730.00 | 1 | 0.002082 |
| 0.03 | 1 | 0.002082 |
| 2575.55 | 1 | 0.002082 |
| 1270.00 | 1 | 0.002082 |
| 267.00 | 1 | 0.002082 |
| 709.00 | 1 | 0.002082 |
| 74000.00 | 1 | 0.002082 |
| 385.00 | 1 | 0.002082 |
| 1559.18 | 1 | 0.002082 |
| 523.00 | 1 | 0.002082 |
| 308.00 | 1 | 0.002082 |
| 28277.52 | 1 | 0.002082 |
| 209.00 | 1 | 0.002082 |
| 458.00 | 1 | 0.002082 |
| 179.00 | 1 | 0.002082 |
| 657.00 | 1 | 0.002082 |
| 439.00 | 1 | 0.002082 |
| 945.70 | 1 | 0.002082 |
| 540.90 | 1 | 0.002082 |
| 451.00 | 1 | 0.002082 |
| 814.00 | 1 | 0.002082 |
| 247.00 | 1 | 0.002082 |
| 383.00 | 1 | 0.002082 |
| 1080.00 | 1 | 0.002082 |
| 287.00 | 1 | 0.002082 |
| 1010.00 | 1 | 0.002082 |
| 1554.00 | 1 | 0.002082 |
| 880.00 | 1 | 0.002082 |
| 2001.00 | 1 | 0.002082 |
| 382.00 | 1 | 0.002082 |
| 469.00 | 1 | 0.002082 |
| 645.00 | 1 | 0.002082 |
| 162.67 | 1 | 0.002082 |
| 224.00 | 1 | 0.002082 |
| 752.00 | 1 | 0.002082 |
| 3420.00 | 1 | 0.002082 |
| 293.00 | 1 | 0.002082 |
| 504.00 | 1 | 0.002082 |
| 936.00 | 1 | 0.002082 |
| 248.00 | 1 | 0.002082 |
| 393.00 | 1 | 0.002082 |
| 895.00 | 1 | 0.002082 |
| 739.00 | 1 | 0.002082 |
| 379.00 | 1 | 0.002082 |
| 312.00 | 1 | 0.002082 |
| 639.00 | 1 | 0.002082 |
| 4100.00 | 1 | 0.002082 |
| 873.00 | 1 | 0.002082 |
| 1773.00 | 1 | 0.002082 |
| 309.00 | 1 | 0.002082 |
| 182.00 | 1 | 0.002082 |
| 3262.00 | 1 | 0.002082 |
| 5636.00 | 1 | 0.002082 |
| 397.00 | 1 | 0.002082 |
| 507.00 | 1 | 0.002082 |
| 1675.00 | 1 | 0.002082 |
| 643.00 | 1 | 0.002082 |
| 328.00 | 1 | 0.002082 |
| 588.00 | 1 | 0.002082 |
| 985.00 | 1 | 0.002082 |
| 584.00 | 1 | 0.002082 |
| 233.00 | 1 | 0.002082 |
| 1830.00 | 1 | 0.002082 |
| 1336.00 | 1 | 0.002082 |
| 1850.00 | 1 | 0.002082 |
| 1128.00 | 1 | 0.002082 |
| 1286.80 | 1 | 0.002082 |
| 398.00 | 1 | 0.002082 |
| 2350.00 | 1 | 0.002082 |
| 1301.00 | 1 | 0.002082 |
| 7600.00 | 1 | 0.002082 |
| 243.00 | 1 | 0.002082 |
| 164.00 | 1 | 0.002082 |
| 795.00 | 1 | 0.002082 |
| 841.00 | 1 | 0.002082 |
| 1443.00 | 1 | 0.002082 |
| 783.00 | 1 | 0.002082 |
| 355.00 | 1 | 0.002082 |
| 405.00 | 1 | 0.002082 |
| 133.03 | 1 | 0.002082 |
| 9500.00 | 1 | 0.002082 |
| 227.00 | 1 | 0.002082 |
| 417.00 | 1 | 0.002082 |
| 461.00 | 1 | 0.002082 |
| 2020.00 | 1 | 0.002082 |
| 492.00 | 1 | 0.002082 |
| 1015.00 | 1 | 0.002082 |
| 494.00 | 1 | 0.002082 |
| 6550.00 | 1 | 0.002082 |
| 686.00 | 1 | 0.002082 |
| 457.00 | 1 | 0.002082 |
| 467.00 | 1 | 0.002082 |
| 692.85 | 1 | 0.002082 |
| 272.00 | 1 | 0.002082 |
| 2190.00 | 1 | 0.002082 |
| 1101.00 | 1 | 0.002082 |
| 890.00 | 1 | 0.002082 |
| 228.70 | 1 | 0.002082 |
| 718.00 | 1 | 0.002082 |
| 5600.00 | 1 | 0.002082 |
| 331.10 | 1 | 0.002082 |
| 371.00 | 1 | 0.002082 |
| 15000.00 | 1 | 0.002082 |
| 228.00 | 1 | 0.002082 |
| 488.00 | 1 | 0.002082 |
| 341.00 | 1 | 0.002082 |
| 368.00 | 1 | 0.002082 |
| 5200.00 | 1 | 0.002082 |
| 359.00 | 1 | 0.002082 |
| 268.00 | 1 | 0.002082 |
| 41.50 | 1 | 0.002082 |
| 816.00 | 1 | 0.002082 |
| 113.00 | 1 | 0.002082 |
| 568.00 | 1 | 0.002082 |
| 576.20 | 1 | 0.002082 |
| 810.00 | 1 | 0.002082 |
| 968.00 | 1 | 0.002082 |
| 29.00 | 1 | 0.002082 |
| 1171.00 | 1 | 0.002082 |
| 6845.00 | 1 | 0.002082 |
| 2802.00 | 1 | 0.002082 |
| 1515.60 | 1 | 0.002082 |
| 1473.00 | 1 | 0.002082 |
| 1273.00 | 1 | 0.002082 |
| 298.00 | 1 | 0.002082 |
| 324.98 | 1 | 0.002082 |
| 3450.00 | 1 | 0.002082 |
| 1340.00 | 1 | 0.002082 |
| 1570.00 | 1 | 0.002082 |
| 193.00 | 1 | 0.002082 |
| 184.00 | 1 | 0.002082 |
| 274.00 | 1 | 0.002082 |
| 2177.00 | 1 | 0.002082 |
| 352.00 | 1 | 0.002082 |
| 548.00 | 1 | 0.002082 |
| 384.00 | 1 | 0.002082 |
| 5003.00 | 1 | 0.002082 |
| 566.00 | 1 | 0.002082 |
| 860.00 | 1 | 0.002082 |
| 806.00 | 1 | 0.002082 |
| 958.00 | 1 | 0.002082 |
| 1209.00 | 1 | 0.002082 |
| 83.00 | 1 | 0.002082 |
| 317.00 | 1 | 0.002082 |
| 339.00 | 1 | 0.002082 |
| 1217.00 | 1 | 0.002082 |
| 726.00 | 1 | 0.002082 |
| 196.00 | 1 | 0.002082 |
| 0.57 | 1 | 0.002082 |
| 431.00 | 1 | 0.002082 |
| 434.00 | 1 | 0.002082 |
| 213.00 | 1 | 0.002082 |
| 344.00 | 1 | 0.002082 |
| 180.50 | 1 | 0.002082 |
| 929.00 | 1 | 0.002082 |
| 3936.00 | 1 | 0.002082 |
| 369.00 | 1 | 0.002082 |
| 893.00 | 1 | 0.002082 |
| 2250.00 | 1 | 0.002082 |
| 40000.00 | 1 | 0.002082 |
| 4300.00 | 1 | 0.002082 |
| 4180.00 | 1 | 0.002082 |
| 655.00 | 1 | 0.002082 |
| 1526.00 | 1 | 0.002082 |
| 623.00 | 1 | 0.002082 |
| 377.00 | 1 | 0.002082 |
| 648.00 | 1 | 0.002082 |
| 194.00 | 1 | 0.002082 |
| 591.00 | 1 | 0.002082 |
| 137.00 | 1 | 0.002082 |
| 479.00 | 1 | 0.002082 |
| 1153.00 | 1 | 0.002082 |
| 3.30 | 1 | 0.002082 |
| 1215.00 | 1 | 0.002082 |
| 167.00 | 1 | 0.002082 |
| 304.00 | 1 | 0.002082 |
| 695.00 | 1 | 0.002082 |
| 581.00 | 1 | 0.002082 |
| 421.00 | 1 | 0.002082 |
| 1.59 | 1 | 0.002082 |
| 0.02 | 1 | 0.002082 |
| 414.00 | 1 | 0.002082 |
| 889.00 | 1 | 0.002082 |
| 236.00 | 1 | 0.002082 |
| 259.00 | 1 | 0.002082 |
| 2450.00 | 1 | 0.002082 |
| 558.00 | 1 | 0.002082 |
| 1001.00 | 1 | 0.002082 |
| 3700.00 | 1 | 0.002082 |
| 1875.00 | 1 | 0.002082 |
| 7300.00 | 1 | 0.002082 |
| 2050.00 | 1 | 0.002082 |
| 508.00 | 1 | 0.002082 |
| 172.00 | 1 | 0.002082 |
| 3901.00 | 1 | 0.002082 |
| 1375.00 | 1 | 0.002082 |
| 1315.00 | 1 | 0.002082 |
| 1525.00 | 1 | 0.002082 |
| 518.00 | 1 | 0.002082 |
| 665.00 | 1 | 0.002082 |
| 674.00 | 1 | 0.002082 |
| 10.10 | 1 | 0.002082 |
| 7.38 | 1 | 0.002082 |
| 327.00 | 1 | 0.002082 |
| 579.00 | 1 | 0.002082 |
| 229.00 | 1 | 0.002082 |
| 1418.00 | 1 | 0.002082 |
| 17000.00 | 1 | 0.002082 |
| 680.00 | 1 | 0.002082 |
| 1120.00 | 1 | 0.002082 |
| 486.00 | 1 | 0.002082 |
| 1620.00 | 1 | 0.002082 |
| 2185.00 | 1 | 0.002082 |
| 881.00 | 1 | 0.002082 |
| 303.00 | 1 | 0.002082 |
| 5700.00 | 1 | 0.002082 |
| 970.00 | 1 | 0.002082 |
| 2575.00 | 1 | 0.002082 |
| 1578.00 | 1 | 0.002082 |
| 294.00 | 1 | 0.002082 |
| 485.00 | 1 | 0.002082 |
| 459.00 | 1 | 0.002082 |
| 335.00 | 1 | 0.002082 |
| 363.00 | 1 | 0.002082 |
| 2570.00 | 1 | 0.002082 |
| 1070.00 | 1 | 0.002082 |
| 254.00 | 1 | 0.002082 |
| 389.00 | 1 | 0.002082 |
| 1725.00 | 1 | 0.002082 |
| 1379.00 | 1 | 0.002082 |
| 296.00 | 1 | 0.002082 |
# Vamos a realizar analisis por cada variable
var = "msf_maximumdonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_maximumdonorvalue__c es 301370. Lo que supone un 62.49585254985235% El nº de vacios para la variable msf_maximumdonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 60.00 | 22402 | 12.386787 |
| 100.00 | 22381 | 12.375176 |
| 30.00 | 18983 | 10.496312 |
| 50.00 | 16425 | 9.081911 |
| 20.00 | 13282 | 7.344045 |
| ... | ... | ... |
| 1502.00 | 1 | 0.000553 |
| 303.59 | 1 | 0.000553 |
| 249.01 | 1 | 0.000553 |
| 267.00 | 1 | 0.000553 |
| 1.17 | 1 | 0.000553 |
1500 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_averagedonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_averagedonorvalue__c es 301370. Lo que supone un 62.49585254985235% El nº de vacios para la variable msf_averagedonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 30.00 | 13617 | 7.529278 |
| 60.00 | 11141 | 6.160218 |
| 20.00 | 10202 | 5.641014 |
| 50.00 | 8951 | 4.949296 |
| 100.00 | 7915 | 4.376458 |
| ... | ... | ... |
| 202.29 | 1 | 0.000553 |
| 282.86 | 1 | 0.000553 |
| 355.49 | 1 | 0.000553 |
| 92.40 | 1 | 0.000553 |
| 232.77 | 1 | 0.000553 |
14837 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lifetime__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_lifetime__c es 825. Lo que supone un 0.17108231859053055% El nº de vacios para la variable msf_lifetime__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 8.0 | 37707 | 7.832796 |
| 7.0 | 37464 | 7.782318 |
| 6.0 | 35559 | 7.386596 |
| 5.0 | 28262 | 5.870806 |
| 0.0 | 24529 | 5.095357 |
| 9.0 | 24307 | 5.049242 |
| 4.0 | 22817 | 4.739727 |
| 3.0 | 20393 | 4.236195 |
| 11.0 | 19939 | 4.141886 |
| 10.0 | 19621 | 4.075829 |
| 12.0 | 18820 | 3.909439 |
| 1.0 | 18237 | 3.788334 |
| 13.0 | 17727 | 3.682392 |
| 14.0 | 16578 | 3.443713 |
| 2.0 | 16485 | 3.424394 |
| 18.0 | 14209 | 2.951606 |
| 17.0 | 13801 | 2.866853 |
| 16.0 | 12218 | 2.538019 |
| 19.0 | 11190 | 2.324475 |
| 15.0 | 10417 | 2.163901 |
| 28.0 | 9775 | 2.030540 |
| 20.0 | 8964 | 1.862073 |
| 23.0 | 7261 | 1.508312 |
| 22.0 | 5446 | 1.131286 |
| 24.0 | 5255 | 1.091610 |
| 29.0 | 5218 | 1.083924 |
| 21.0 | 3937 | 0.817825 |
| 25.0 | 3885 | 0.807023 |
| 30.0 | 3582 | 0.744081 |
| 27.0 | 3374 | 0.700874 |
| 26.0 | 3312 | 0.687995 |
| 31.0 | 667 | 0.138555 |
| 32.0 | 173 | 0.035937 |
| 34.0 | 133 | 0.027628 |
| 33.0 | 80 | 0.016618 |
| 35.0 | 43 | 0.008932 |
| 36.0 | 14 | 0.002908 |
# Vamos a realizar analisis por cada variable
var = "msf_commitment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos_f)
El nº de nulos para la variable msf_commitment__c es 4048. Lo que supone un 0.8394439098842031% El nº de vacios para la variable msf_commitment__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 273596 | 57.216590 |
| 1.0 | 102301 | 21.394006 |
| 2.0 | 47492 | 9.931908 |
| 3.0 | 23798 | 4.976829 |
| 4.0 | 12718 | 2.659690 |
| 5.0 | 7030 | 1.470170 |
| 6.0 | 4175 | 0.873109 |
| 7.0 | 2481 | 0.518847 |
| 8.0 | 1512 | 0.316202 |
| 9.0 | 963 | 0.201390 |
| 10.0 | 583 | 0.121922 |
| 11.0 | 433 | 0.090552 |
| 12.0 | 268 | 0.056046 |
| 13.0 | 194 | 0.040571 |
| 14.0 | 132 | 0.027605 |
| 15.0 | 95 | 0.019867 |
| 16.0 | 93 | 0.019449 |
| 17.0 | 59 | 0.012339 |
| 18.0 | 44 | 0.009202 |
| 19.0 | 31 | 0.006483 |
| 20.0 | 26 | 0.005437 |
| 21.0 | 23 | 0.004810 |
| 22.0 | 19 | 0.003973 |
| 23.0 | 14 | 0.002928 |
| 25.0 | 10 | 0.002091 |
| 29.0 | 10 | 0.002091 |
| 26.0 | 8 | 0.001673 |
| 24.0 | 8 | 0.001673 |
| 28.0 | 8 | 0.001673 |
| 27.0 | 7 | 0.001464 |
| 32.0 | 7 | 0.001464 |
| 30.0 | 6 | 0.001255 |
| 31.0 | 6 | 0.001255 |
| 33.0 | 4 | 0.000837 |
| 34.0 | 3 | 0.000627 |
| 36.0 | 2 | 0.000418 |
| 42.0 | 2 | 0.000418 |
| 61.0 | 2 | 0.000418 |
| 38.0 | 2 | 0.000418 |
| 47.0 | 1 | 0.000209 |
| 43.0 | 1 | 0.000209 |
| 80.0 | 1 | 0.000209 |
| 57.0 | 1 | 0.000209 |
| 56.0 | 1 | 0.000209 |
| 37.0 | 1 | 0.000209 |
| 71.0 | 1 | 0.000209 |
| 35.0 | 1 | 0.000209 |
| 46.0 | 1 | 0.000209 |
| 45.0 | 1 | 0.000209 |
| 54.0 | 1 | 0.000209 |
# Se analizar solo los contactos que tienen donaciones recurrentes, ya que son el objetivo del analisis, por ello solo se tendrán en cuanta los contactos coincidentes entre la tabla recurring donation y contactos
df_contactos_f = df_contactos[df_contactos.id.isin(df_re_donation.npe03__contact__c)]
# Vamos a analizar la tabla recurring donation
df=df_contactos_f
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_contactos=list()
# Vamos a realizar analisis por cada variable
var = "msf_seniority__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_seniority__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_seniority__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 72843 | 7.327798 |
| 8.0 | 69373 | 6.978726 |
| 7.0 | 68120 | 6.852677 |
| 6.0 | 65874 | 6.626736 |
| 9.0 | 64102 | 6.448478 |
| 5.0 | 47211 | 4.749292 |
| 10.0 | 43910 | 4.417221 |
| 12.0 | 43340 | 4.359880 |
| 4.0 | 39932 | 4.017045 |
| 13.0 | 38984 | 3.921679 |
| 11.0 | 37505 | 3.772896 |
| 14.0 | 36133 | 3.634877 |
| 18.0 | 34795 | 3.500278 |
| 2.0 | 31380 | 3.156738 |
| 17.0 | 31370 | 3.155732 |
| 1.0 | 30543 | 3.072539 |
| 16.0 | 30132 | 3.031193 |
| 15.0 | 28826 | 2.899813 |
| 3.0 | 27544 | 2.770848 |
| 19.0 | 26257 | 2.641379 |
| 29.0 | 19570 | 1.968686 |
| 20.0 | 18961 | 1.907422 |
| 23.0 | 13852 | 1.393472 |
| 22.0 | 10824 | 1.088863 |
| 21.0 | 9968 | 1.002752 |
| 24.0 | 9882 | 0.994101 |
| 28.0 | 9759 | 0.981728 |
| 25.0 | 9327 | 0.938270 |
| 27.0 | 6960 | 0.700156 |
| 31.0 | 6128 | 0.616459 |
| 26.0 | 4656 | 0.468380 |
| 30.0 | 4424 | 0.445042 |
| 32.0 | 881 | 0.088626 |
| 35.0 | 216 | 0.021729 |
| 34.0 | 215 | 0.021628 |
| 33.0 | 171 | 0.017202 |
| 36.0 | 84 | 0.008450 |
| 37.0 | 12 | 0.001207 |
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year__c"
# Analizamos nulos
count_nulos(df_contactos,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__best_gift_year__c es 709207. Lo que supone un 39.32569192184401%
['npo02__best_gift_year__c']
# Analizamos posibles valores de la variable
freq_variables(df_contactos,var)
| # Tot | % Tot | |
|---|---|---|
| 709207 | 39.325692 | |
| 2018 | 303667 | 16.838405 |
| 2022 | 185032 | 10.260067 |
| 2021 | 93074 | 5.160975 |
| 2020 | 90828 | 5.036434 |
| 2019 | 77054 | 4.272662 |
| 2023 | 55899 | 3.099612 |
| 2010 | 29210 | 1.619701 |
| 1994 | 28224 | 1.565027 |
| 2017 | 21245 | 1.178040 |
| 2005 | 15932 | 0.883433 |
| 2014 | 14681 | 0.814065 |
| 2011 | 14643 | 0.811958 |
| 2004 | 13160 | 0.729725 |
| 2000 | 12659 | 0.701944 |
| 2015 | 11996 | 0.665181 |
| 2001 | 11403 | 0.632299 |
| 1998 | 11363 | 0.630081 |
| 2013 | 10940 | 0.606626 |
| 2016 | 9948 | 0.551619 |
| 2003 | 9537 | 0.528829 |
| 2008 | 8465 | 0.469386 |
| 1999 | 8142 | 0.451476 |
| 2009 | 7599 | 0.421366 |
| 1996 | 6869 | 0.380888 |
| 2012 | 6795 | 0.376784 |
| 2006 | 6723 | 0.372792 |
| 1992 | 6238 | 0.345899 |
| 2007 | 5562 | 0.308414 |
| 2002 | 4753 | 0.263555 |
| 1997 | 4491 | 0.249027 |
| 1995 | 4064 | 0.225350 |
| 1993 | 2470 | 0.136962 |
| 1991 | 624 | 0.034601 |
| 1989 | 435 | 0.024121 |
| 1990 | 212 | 0.011755 |
| 1988 | 187 | 0.010369 |
| 1987 | 88 | 0.004880 |
# Vamos a realizar analisis por cada variable
var = "msf_birthyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_birthyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_birthyear__c es 216719. Lo que supone un 21.8013125915434%
['npo02__best_gift_year__c', 'msf_birthyear__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 216719 | 21.801313 | |
| 1964 | 17594 | 1.769906 |
| 1963 | 17337 | 1.744053 |
| 1965 | 17330 | 1.743349 |
| 1968 | 17142 | 1.724436 |
| 1959 | 17014 | 1.711560 |
| 1966 | 16945 | 1.704619 |
| 1958 | 16925 | 1.702607 |
| 1962 | 16912 | 1.701299 |
| 1973 | 16892 | 1.699287 |
| 1975 | 16872 | 1.697275 |
| 1974 | 16840 | 1.694056 |
| 1961 | 16738 | 1.683795 |
| 1960 | 16698 | 1.679771 |
| 1972 | 16694 | 1.679369 |
| 1957 | 16680 | 1.677960 |
| 1967 | 16658 | 1.675747 |
| 1976 | 16577 | 1.667599 |
| 1969 | 16530 | 1.662871 |
| 1971 | 16525 | 1.662368 |
| 1970 | 16374 | 1.647178 |
| 1977 | 16072 | 1.616797 |
| 1978 | 15831 | 1.592553 |
| 1956 | 15179 | 1.526964 |
| 1979 | 15167 | 1.525757 |
| 1980 | 14467 | 1.455339 |
| 1955 | 14076 | 1.416005 |
| 1981 | 13669 | 1.375062 |
| 1954 | 13015 | 1.309272 |
| 1982 | 12607 | 1.268228 |
| 1953 | 12371 | 1.244487 |
| 1983 | 11938 | 1.200929 |
| 1952 | 11928 | 1.199923 |
| 1984 | 11031 | 1.109687 |
| 1951 | 10886 | 1.095101 |
| 1950 | 10448 | 1.051039 |
| 1985 | 10209 | 1.026996 |
| 1949 | 10096 | 1.015629 |
| 1948 | 9717 | 0.977502 |
| 1986 | 9190 | 0.924488 |
| 1987 | 8599 | 0.865035 |
| 1947 | 8534 | 0.858496 |
| 1988 | 7972 | 0.801960 |
| 1946 | 7749 | 0.779527 |
| 1945 | 7715 | 0.776107 |
| 1989 | 7665 | 0.771077 |
| 1990 | 7188 | 0.723092 |
| 1991 | 7174 | 0.721684 |
| 1992 | 7100 | 0.714240 |
| 1996 | 6956 | 0.699754 |
| 1994 | 6952 | 0.699351 |
| 1995 | 6929 | 0.697038 |
| 1993 | 6812 | 0.685268 |
| 1997 | 6806 | 0.684664 |
| 1943 | 6730 | 0.677019 |
| 1944 | 6682 | 0.672190 |
| 1999 | 6496 | 0.653479 |
| 1998 | 6406 | 0.644425 |
| 2000 | 6137 | 0.617365 |
| 2001 | 5321 | 0.535277 |
| 1942 | 5128 | 0.515862 |
| 1940 | 4817 | 0.484576 |
| 1941 | 4598 | 0.462546 |
| 2002 | 4409 | 0.443533 |
| 2003 | 3408 | 0.342835 |
| 1936 | 3275 | 0.329456 |
| 1938 | 3130 | 0.314869 |
| 1939 | 3080 | 0.309839 |
| 1937 | 3037 | 0.305514 |
| 1935 | 2925 | 0.294247 |
| 1934 | 2560 | 0.257529 |
| 1933 | 2251 | 0.226444 |
| 1932 | 2104 | 0.211656 |
| 2004 | 1909 | 0.192040 |
| 1930 | 1864 | 0.187513 |
| 1931 | 1800 | 0.181075 |
| 1929 | 1306 | 0.131380 |
| 1928 | 1202 | 0.120918 |
| 1927 | 966 | 0.097177 |
| 1926 | 803 | 0.080780 |
| 1925 | 717 | 0.072128 |
| 1924 | 623 | 0.062672 |
| 1923 | 478 | 0.048085 |
| 1922 | 453 | 0.045571 |
| 1921 | 336 | 0.033801 |
| 2020 | 276 | 0.027765 |
| 1920 | 258 | 0.025954 |
| 1919 | 235 | 0.023640 |
| 2005 | 216 | 0.021729 |
| 2006 | 157 | 0.015794 |
| 1918 | 150 | 0.015090 |
| 2017 | 129 | 0.012977 |
| 1917 | 123 | 0.012373 |
| 2008 | 117 | 0.011770 |
| 2016 | 113 | 0.011367 |
| 2007 | 110 | 0.011066 |
| 2019 | 107 | 0.010764 |
| 1916 | 98 | 0.009859 |
| 2014 | 88 | 0.008853 |
| 2015 | 84 | 0.008450 |
| 2013 | 81 | 0.008148 |
| 2018 | 73 | 0.007344 |
| 2021 | 69 | 0.006941 |
| 1915 | 68 | 0.006841 |
| 2010 | 67 | 0.006740 |
| 2012 | 66 | 0.006639 |
| 2009 | 65 | 0.006539 |
| 1914 | 59 | 0.005935 |
| 2011 | 56 | 0.005633 |
| 1913 | 40 | 0.004024 |
| 1911 | 29 | 0.002917 |
| 1912 | 20 | 0.002012 |
| 2022 | 18 | 0.001811 |
| 1910 | 17 | 0.001710 |
| 1909 | 14 | 0.001408 |
| 2023 | 13 | 0.001308 |
| 1907 | 9 | 0.000905 |
| 1904 | 8 | 0.000805 |
| 1906 | 8 | 0.000805 |
| 1908 | 8 | 0.000805 |
| 1902 | 5 | 0.000503 |
| 1903 | 4 | 0.000402 |
| 1901 | 3 | 0.000302 |
| 1900 | 3 | 0.000302 |
| 1905 | 2 | 0.000201 |
| 1712 | 1 | 0.000101 |
| 1893 | 1 | 0.000101 |
| 1897 | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_entrycampaign__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrycampaign__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_entrycampaign__c es 74. Lo que supone un 0.007444188704147821%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mr4CQAQ | 37526 | 3.775008 |
| 7013Y000001mrCzQAI | 36910 | 3.713041 |
| 7013Y000001mr2DQAQ | 30523 | 3.070527 |
| 7013Y000001mr2cQAA | 25776 | 2.592992 |
| 7013Y000001mrBSQAY | 24519 | 2.466541 |
| ... | ... | ... |
| 7013Y000001mrJ5QAI | 1 | 0.000101 |
| 7013Y000001gt85QAA | 1 | 0.000101 |
| 7013Y000001n7z7QAA | 1 | 0.000101 |
| 7013Y000001mrEWQAY | 1 | 0.000101 |
| 7013Y000001mre3QAA | 1 | 0.000101 |
2987 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "leadsource"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable leadsource es 0. Lo que supone un 0.0% El nº de vacios para la variable leadsource es 20. Lo que supone un 0.0020119428930129245%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Persona a persona | 346349 | 34.841720 |
| Otro | 229312 | 23.068132 |
| Telemarketing | 153973 | 15.489244 |
| Personal con tablet | 70560 | 7.098135 |
| Cupón | 65399 | 6.578953 |
| Web MSF | 64678 | 6.506422 |
| Teléfono campaña | 34878 | 3.508627 |
| Web terceros | 14767 | 1.485518 |
| Web campaña | 4795 | 0.482363 |
| Teléfono web | 4304 | 0.432970 |
| Eventos | 1969 | 0.198076 |
| Teléfono SAS | 1524 | 0.153310 |
| Email a SAS | 921 | 0.092650 |
| Email a Empresas | 138 | 0.013882 |
| Plataforma iniciativas | 127 | 0.012776 |
| Email a Bodas | 124 | 0.012474 |
| Correo postal sin cupón | 92 | 0.009255 |
| Entidad financiera | 87 | 0.008752 |
| Teléfono Officers | 23 | 0.002314 |
| 20 | 0.002012 | |
| Teléfono Herencias y Legados | 4 | 0.000402 |
| Email a Iniciativas Solidarias | 3 | 0.000302 |
| Cloud page | 3 | 0.000302 |
| Email herencias | 3 | 0.000302 |
| Email a One to one | 3 | 0.000302 |
| Email a officers Mid Donors | 2 | 0.000201 |
| Tel?fono SAS | 2 | 0.000201 |
| Email Director/a General | 1 | 0.000101 |
| SMS | 1 | 0.000101 |
| TelEfono officers | 1 | 0.000101 |
| Redes Sociales | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaigncolaborationchannel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaigncolaborationchannel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaigncolaborationchannel__c es 139277. Lo que supone un 14.010868515508058%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Persona a persona | 310662 | 31.251710 |
| Telemarketing | 162367 | 16.333657 |
| 139277 | 14.010869 | |
| Otro | 113431 | 11.410835 |
| Cupón | 67920 | 6.832558 |
| Web MSF | 67860 | 6.826522 |
| Personal con tablet | 65618 | 6.600983 |
| Teléfono campaña | 36839 | 3.705898 |
| Web terceros | 12878 | 1.295490 |
| Teléfono web | 4501 | 0.452788 |
| Web campaña | 4339 | 0.436491 |
| Teléfono SAS | 2498 | 0.251292 |
| Email a SAS | 1437 | 0.144558 |
| Eventos | 1435 | 0.144357 |
| Plataforma iniciativas | 1006 | 0.101201 |
| web campaña | 624 | 0.062773 |
| Entidad financiera | 583 | 0.058648 |
| cupón | 224 | 0.022534 |
| Web MSF Mi perfil | 141 | 0.014184 |
| Email a Empresas | 123 | 0.012373 |
| Correo postal sin cupón | 119 | 0.011971 |
| Email a Bodas | 103 | 0.010362 |
| Teléfono Officers | 63 | 0.006338 |
| Email a officers Mid Donors | 4 | 0.000402 |
| Email a Iniciativas Solidarias | 4 | 0.000402 |
| Email a One to one | 3 | 0.000302 |
| Cloud page | 2 | 0.000201 |
| Email Director/a General | 1 | 0.000101 |
| Email a one to one | 1 | 0.000101 |
| Email herencias | 1 | 0.000101 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_firstcampaigncolaborationchannel__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c']
# Vamos a realizar analisis por cada variable
var = "npo02__averageamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__averageamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__averageamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 994064 | 100.0 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("npo02__averageamount__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c']
# Vamos a realizar analisis por cada variable
var = "msf_isactivedonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isactivedonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Nunca | 727574 | 73.191867 |
| Exdonante | 212374 | 21.364218 |
| Donante | 54116 | 5.443915 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactivedonor__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c']
# Vamos a realizar analisis por cada variable
var = "msf_isactiverecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isactiverecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Baja | 511080 | 51.413189 |
| Socio | 482224 | 48.510357 |
| Nunca | 760 | 0.076454 |
# Se va a añadir esta variable a la lista de columnas a borrar
col_to_delete_contactos.append("msf_isactiverecurringdonor__c")
col_to_delete_contactos
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c']
# Vamos a realizar analisis por cada variable
var = "npsp__deceased__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__deceased__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npsp__deceased__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 971783 | 97.758595 |
| True | 22281 | 2.241405 |
# Vamos a realizar analisis por cada variable
var = "msf_begindatemsf__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_begindatemsf__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2004-01-01 | 4833 | 0.486186 |
| 2000-02-01 | 4462 | 0.448864 |
| 2000-01-01 | 3903 | 0.392631 |
| 1994-10-01 | 3486 | 0.350682 |
| 1995-02-01 | 3386 | 0.340622 |
| ... | ... | ... |
| 1996-05-13 | 1 | 0.000101 |
| 1996-03-28 | 1 | 0.000101 |
| 1996-10-27 | 1 | 0.000101 |
| 1996-07-31 | 1 | 0.000101 |
| 1994-09-18 | 1 | 0.000101 |
10115 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_fechacambiolevelrelacion__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_fechacambiolevelrelacion__c es 4. Lo que supone un 0.00040238857860258495% El nº de vacios para la variable msf_fechacambiolevelrelacion__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-03-28 | 852024 | 85.711526 |
| 2020-07-20 | 6964 | 0.700561 |
| 2020-09-22 | 4511 | 0.453796 |
| 2020-09-19 | 3664 | 0.368589 |
| 2022-01-02 | 2462 | 0.247671 |
| 2020-09-21 | 1639 | 0.164879 |
| 2023-01-03 | 1481 | 0.148985 |
| 2020-09-20 | 1466 | 0.147476 |
| 2021-01-04 | 1023 | 0.102911 |
| 2023-01-02 | 777 | 0.078164 |
| 2022-01-15 | 707 | 0.071122 |
| 2022-05-06 | 536 | 0.053920 |
| 2022-03-22 | 529 | 0.053216 |
| 2020-12-04 | 522 | 0.052512 |
| 2022-06-04 | 491 | 0.049393 |
| 2021-06-18 | 472 | 0.047482 |
| 2022-12-03 | 461 | 0.046375 |
| 2022-05-11 | 445 | 0.044766 |
| 2021-03-04 | 439 | 0.044162 |
| 2022-12-23 | 434 | 0.043659 |
| 2020-10-03 | 431 | 0.043358 |
| 2020-09-23 | 425 | 0.042754 |
| 2022-10-21 | 418 | 0.042050 |
| 2023-02-10 | 403 | 0.040541 |
| 2021-07-08 | 398 | 0.040038 |
| 2021-02-05 | 395 | 0.039736 |
| 2022-09-23 | 387 | 0.038931 |
| 2021-08-07 | 380 | 0.038227 |
| 2022-02-05 | 379 | 0.038126 |
| 2022-11-24 | 376 | 0.037825 |
| 2021-06-25 | 374 | 0.037623 |
| 2021-11-18 | 373 | 0.037523 |
| 2022-03-11 | 371 | 0.037322 |
| 2020-09-25 | 371 | 0.037322 |
| 2021-03-11 | 368 | 0.037020 |
| 2021-01-03 | 366 | 0.036819 |
| 2021-05-13 | 365 | 0.036718 |
| 2023-02-23 | 361 | 0.036316 |
| 2023-02-09 | 356 | 0.035813 |
| 2020-11-20 | 349 | 0.035109 |
| 2020-11-27 | 349 | 0.035109 |
| 2022-03-05 | 347 | 0.034907 |
| 2022-05-19 | 334 | 0.033600 |
| 2022-03-12 | 333 | 0.033499 |
| 2023-01-26 | 332 | 0.033398 |
| 2022-03-09 | 329 | 0.033097 |
| 2020-11-06 | 325 | 0.032694 |
| 2021-05-08 | 322 | 0.032392 |
| 2023-03-30 | 322 | 0.032392 |
| 2022-09-08 | 318 | 0.031990 |
| 2021-12-03 | 312 | 0.031386 |
| 2023-02-21 | 311 | 0.031286 |
| 2022-11-18 | 309 | 0.031085 |
| 2021-01-28 | 308 | 0.030984 |
| 2021-03-18 | 308 | 0.030984 |
| 2023-02-17 | 308 | 0.030984 |
| 2022-07-07 | 307 | 0.030883 |
| 2022-03-10 | 306 | 0.030783 |
| 2021-03-06 | 304 | 0.030582 |
| 2021-11-07 | 303 | 0.030481 |
| 2021-11-13 | 301 | 0.030280 |
| 2022-06-17 | 298 | 0.029978 |
| 2021-05-27 | 298 | 0.029978 |
| 2021-02-11 | 295 | 0.029676 |
| 2021-05-20 | 294 | 0.029576 |
| 2022-12-20 | 294 | 0.029576 |
| 2021-01-21 | 288 | 0.028972 |
| 2023-02-15 | 285 | 0.028670 |
| 2020-09-24 | 285 | 0.028670 |
| 2022-10-06 | 278 | 0.027966 |
| 2021-04-17 | 277 | 0.027866 |
| 2022-05-28 | 276 | 0.027765 |
| 2021-04-29 | 275 | 0.027664 |
| 2021-07-03 | 274 | 0.027564 |
| 2022-03-17 | 273 | 0.027463 |
| 2023-04-05 | 272 | 0.027363 |
| 2022-03-04 | 271 | 0.027262 |
| 2021-04-22 | 270 | 0.027161 |
| 2020-10-24 | 268 | 0.026960 |
| 2021-06-09 | 268 | 0.026960 |
| 2020-09-29 | 266 | 0.026759 |
| 2021-05-29 | 264 | 0.026558 |
| 2022-12-15 | 264 | 0.026558 |
| 2021-02-18 | 264 | 0.026558 |
| 2021-03-25 | 264 | 0.026558 |
| 2023-05-26 | 260 | 0.026155 |
| 2021-12-17 | 260 | 0.026155 |
| 2022-12-04 | 260 | 0.026155 |
| 2022-11-10 | 257 | 0.025854 |
| 2021-09-04 | 256 | 0.025753 |
| 2021-11-30 | 255 | 0.025652 |
| 2023-02-16 | 255 | 0.025652 |
| 2021-06-29 | 252 | 0.025351 |
| 2020-10-29 | 251 | 0.025250 |
| 2022-03-15 | 251 | 0.025250 |
| 2020-10-22 | 250 | 0.025149 |
| 2021-06-05 | 250 | 0.025149 |
| 2022-11-17 | 248 | 0.024948 |
| 2022-04-12 | 246 | 0.024747 |
| 2022-03-24 | 245 | 0.024646 |
| 2021-06-03 | 244 | 0.024546 |
| 2022-09-28 | 244 | 0.024546 |
| 2023-02-08 | 243 | 0.024445 |
| 2023-03-10 | 243 | 0.024445 |
| 2020-11-17 | 243 | 0.024445 |
| 2022-01-28 | 242 | 0.024345 |
| 2022-02-03 | 242 | 0.024345 |
| 2022-11-30 | 242 | 0.024345 |
| 2022-03-16 | 241 | 0.024244 |
| 2021-12-22 | 239 | 0.024043 |
| 2022-09-30 | 237 | 0.023842 |
| 2021-04-01 | 237 | 0.023842 |
| 2023-02-12 | 237 | 0.023842 |
| 2023-07-05 | 236 | 0.023741 |
| 2022-11-11 | 234 | 0.023540 |
| 2023-03-16 | 233 | 0.023439 |
| 2022-05-12 | 232 | 0.023339 |
| 2022-03-08 | 232 | 0.023339 |
| 2021-02-25 | 231 | 0.023238 |
| 2022-03-18 | 231 | 0.023238 |
| 2022-08-06 | 231 | 0.023238 |
| 2022-11-25 | 231 | 0.023238 |
| 2023-02-24 | 231 | 0.023238 |
| 2022-12-16 | 230 | 0.023137 |
| 2023-03-23 | 230 | 0.023137 |
| 2020-10-06 | 229 | 0.023037 |
| 2021-09-09 | 229 | 0.023037 |
| 2022-11-26 | 225 | 0.022634 |
| 2021-02-10 | 225 | 0.022634 |
| 2021-10-29 | 224 | 0.022534 |
| 2022-11-16 | 223 | 0.022433 |
| 2023-05-11 | 223 | 0.022433 |
| 2021-02-17 | 222 | 0.022333 |
| 2023-06-16 | 221 | 0.022232 |
| 2022-03-31 | 221 | 0.022232 |
| 2022-12-10 | 220 | 0.022131 |
| 2020-12-25 | 219 | 0.022031 |
| 2021-04-16 | 219 | 0.022031 |
| 2021-11-25 | 219 | 0.022031 |
| 2021-07-29 | 218 | 0.021930 |
| 2021-07-22 | 217 | 0.021830 |
| 2022-03-25 | 216 | 0.021729 |
| 2023-05-12 | 216 | 0.021729 |
| 2023-03-17 | 216 | 0.021729 |
| 2023-03-31 | 214 | 0.021528 |
| 2023-04-14 | 214 | 0.021528 |
| 2022-07-22 | 214 | 0.021528 |
| 2022-10-20 | 213 | 0.021427 |
| 2022-11-23 | 213 | 0.021427 |
| 2023-01-19 | 213 | 0.021427 |
| 2022-04-09 | 213 | 0.021427 |
| 2020-12-30 | 212 | 0.021327 |
| 2023-04-21 | 212 | 0.021327 |
| 2023-04-07 | 212 | 0.021327 |
| 2021-07-01 | 212 | 0.021327 |
| 2023-02-18 | 210 | 0.021125 |
| 2021-10-03 | 210 | 0.021125 |
| 2022-10-14 | 210 | 0.021125 |
| 2021-10-01 | 210 | 0.021125 |
| 2021-02-24 | 209 | 0.021025 |
| 2022-11-09 | 209 | 0.021025 |
| 2022-11-01 | 209 | 0.021025 |
| 2022-04-28 | 208 | 0.020924 |
| 2021-06-10 | 207 | 0.020824 |
| 2022-10-04 | 206 | 0.020723 |
| 2022-12-17 | 206 | 0.020723 |
| 2023-05-25 | 206 | 0.020723 |
| 2022-10-27 | 205 | 0.020622 |
| 2022-11-08 | 205 | 0.020622 |
| 2022-12-21 | 205 | 0.020622 |
| 2023-02-11 | 205 | 0.020622 |
| 2023-02-01 | 204 | 0.020522 |
| 2021-09-22 | 203 | 0.020421 |
| 2021-11-19 | 202 | 0.020321 |
| 2021-01-14 | 201 | 0.020220 |
| 2020-12-19 | 201 | 0.020220 |
| 2021-03-27 | 201 | 0.020220 |
| 2022-05-13 | 200 | 0.020120 |
| 2022-12-01 | 200 | 0.020120 |
| 2022-11-22 | 200 | 0.020120 |
| 2021-12-23 | 200 | 0.020120 |
| 2023-06-22 | 200 | 0.020120 |
| 2021-11-11 | 199 | 0.020019 |
| 2023-04-19 | 198 | 0.019918 |
| 2022-01-06 | 198 | 0.019918 |
| 2022-10-28 | 198 | 0.019918 |
| 2023-01-27 | 197 | 0.019818 |
| 2022-02-24 | 197 | 0.019818 |
| 2021-10-16 | 197 | 0.019818 |
| 2021-10-23 | 196 | 0.019717 |
| 2021-10-22 | 196 | 0.019717 |
| 2022-04-08 | 196 | 0.019717 |
| 2023-02-28 | 195 | 0.019617 |
| 2023-06-29 | 195 | 0.019617 |
| 2023-04-28 | 195 | 0.019617 |
| 2021-05-06 | 194 | 0.019516 |
| 2022-11-19 | 194 | 0.019516 |
| 2023-06-01 | 194 | 0.019516 |
| 2021-12-14 | 193 | 0.019415 |
| 2022-12-14 | 193 | 0.019415 |
| 2021-07-15 | 193 | 0.019415 |
| 2023-06-09 | 192 | 0.019315 |
| 2023-06-30 | 191 | 0.019214 |
| 2022-06-15 | 191 | 0.019214 |
| 2022-06-23 | 191 | 0.019214 |
| 2023-06-15 | 191 | 0.019214 |
| 2020-10-15 | 191 | 0.019214 |
| 2022-05-20 | 190 | 0.019114 |
| 2023-05-24 | 189 | 0.019013 |
| 2021-09-23 | 189 | 0.019013 |
| 2022-05-10 | 189 | 0.019013 |
| 2020-12-15 | 187 | 0.018812 |
| 2022-02-11 | 187 | 0.018812 |
| 2022-02-18 | 186 | 0.018711 |
| 2023-03-24 | 186 | 0.018711 |
| 2023-01-21 | 186 | 0.018711 |
| 2021-04-08 | 186 | 0.018711 |
| 2023-06-21 | 186 | 0.018711 |
| 2022-10-07 | 185 | 0.018611 |
| 2021-01-06 | 185 | 0.018611 |
| 2023-04-26 | 185 | 0.018611 |
| 2022-11-15 | 185 | 0.018611 |
| 2022-04-29 | 185 | 0.018611 |
| 2023-05-09 | 185 | 0.018611 |
| 2023-06-28 | 184 | 0.018510 |
| 2023-04-27 | 184 | 0.018510 |
| 2023-01-31 | 183 | 0.018409 |
| 2021-06-04 | 183 | 0.018409 |
| 2020-12-11 | 182 | 0.018309 |
| 2022-06-29 | 182 | 0.018309 |
| 2021-05-16 | 181 | 0.018208 |
| 2022-04-01 | 181 | 0.018208 |
| 2021-06-01 | 180 | 0.018108 |
| 2023-07-07 | 180 | 0.018108 |
| 2021-09-24 | 180 | 0.018108 |
| 2022-01-27 | 179 | 0.018007 |
| 2020-10-30 | 179 | 0.018007 |
| 2020-12-17 | 179 | 0.018007 |
| 2023-06-03 | 179 | 0.018007 |
| 2023-02-25 | 179 | 0.018007 |
| 2021-09-16 | 179 | 0.018007 |
| 2023-06-06 | 178 | 0.017906 |
| 2022-11-29 | 178 | 0.017906 |
| 2021-12-16 | 178 | 0.017906 |
| 2023-01-20 | 178 | 0.017906 |
| 2022-07-15 | 178 | 0.017906 |
| 2023-02-07 | 177 | 0.017806 |
| 2023-06-17 | 177 | 0.017806 |
| 2021-10-15 | 177 | 0.017806 |
| 2022-06-10 | 177 | 0.017806 |
| 2023-07-06 | 177 | 0.017806 |
| 2023-02-14 | 176 | 0.017705 |
| 2022-03-23 | 176 | 0.017705 |
| 2023-01-14 | 176 | 0.017705 |
| 2023-06-23 | 176 | 0.017705 |
| 2022-04-14 | 176 | 0.017705 |
| 2021-09-30 | 176 | 0.017705 |
| 2022-10-25 | 175 | 0.017605 |
| 2023-02-05 | 175 | 0.017605 |
| 2022-09-04 | 175 | 0.017605 |
| 2021-04-15 | 175 | 0.017605 |
| 2022-05-27 | 175 | 0.017605 |
| 2022-03-29 | 175 | 0.017605 |
| 2021-10-19 | 175 | 0.017605 |
| 2023-05-31 | 175 | 0.017605 |
| 2022-07-14 | 174 | 0.017504 |
| 2021-02-03 | 174 | 0.017504 |
| 2020-12-22 | 174 | 0.017504 |
| 2023-06-08 | 174 | 0.017504 |
| 2022-06-09 | 174 | 0.017504 |
| 2021-05-21 | 173 | 0.017403 |
| 2021-10-26 | 173 | 0.017403 |
| 2022-05-14 | 173 | 0.017403 |
| 2022-07-21 | 173 | 0.017403 |
| 2020-10-20 | 172 | 0.017303 |
| 2023-01-06 | 172 | 0.017303 |
| 2022-06-08 | 172 | 0.017303 |
| 2022-10-12 | 171 | 0.017202 |
| 2021-02-23 | 171 | 0.017202 |
| 2021-04-23 | 171 | 0.017202 |
| 2020-11-13 | 171 | 0.017202 |
| 2020-10-10 | 171 | 0.017202 |
| 2021-02-06 | 170 | 0.017102 |
| 2023-01-12 | 170 | 0.017102 |
| 2021-11-16 | 170 | 0.017102 |
| 2023-05-18 | 170 | 0.017102 |
| 2023-03-09 | 170 | 0.017102 |
| 2023-05-27 | 170 | 0.017102 |
| 2022-02-26 | 170 | 0.017102 |
| 2021-12-01 | 170 | 0.017102 |
| 2023-01-28 | 169 | 0.017001 |
| 2022-10-15 | 168 | 0.016900 |
| 2021-02-26 | 168 | 0.016900 |
| 2020-10-14 | 168 | 0.016900 |
| 2022-06-24 | 168 | 0.016900 |
| 2022-10-26 | 167 | 0.016800 |
| 2021-12-19 | 167 | 0.016800 |
| 2022-11-12 | 166 | 0.016699 |
| 2021-12-21 | 166 | 0.016699 |
| 2023-05-07 | 166 | 0.016699 |
| 2023-01-04 | 166 | 0.016699 |
| 2023-02-03 | 165 | 0.016599 |
| 2021-12-15 | 165 | 0.016599 |
| 2021-11-27 | 165 | 0.016599 |
| 2022-07-13 | 164 | 0.016498 |
| 2022-07-10 | 164 | 0.016498 |
| 2023-01-13 | 164 | 0.016498 |
| 2020-12-03 | 164 | 0.016498 |
| 2022-07-28 | 164 | 0.016498 |
| 2023-05-19 | 163 | 0.016397 |
| 2021-10-21 | 163 | 0.016397 |
| 2022-03-01 | 163 | 0.016397 |
| 2022-07-01 | 163 | 0.016397 |
| 2022-02-17 | 162 | 0.016297 |
| 2021-02-16 | 161 | 0.016196 |
| 2020-12-14 | 161 | 0.016196 |
| 2021-05-28 | 161 | 0.016196 |
| 2023-03-01 | 161 | 0.016196 |
| 2022-05-17 | 161 | 0.016196 |
| 2021-10-09 | 160 | 0.016096 |
| 2021-10-07 | 160 | 0.016096 |
| 2023-03-08 | 160 | 0.016096 |
| 2022-09-15 | 160 | 0.016096 |
| 2023-06-24 | 160 | 0.016096 |
| 2023-04-22 | 159 | 0.015995 |
| 2021-06-19 | 159 | 0.015995 |
| 2023-01-25 | 159 | 0.015995 |
| 2022-04-22 | 159 | 0.015995 |
| 2022-10-18 | 159 | 0.015995 |
| 2021-02-04 | 159 | 0.015995 |
| 2022-01-19 | 158 | 0.015894 |
| 2021-01-20 | 158 | 0.015894 |
| 2022-06-01 | 158 | 0.015894 |
| 2021-09-15 | 158 | 0.015894 |
| 2023-05-13 | 157 | 0.015794 |
| 2022-02-16 | 157 | 0.015794 |
| 2020-10-16 | 157 | 0.015794 |
| 2022-07-08 | 156 | 0.015693 |
| 2021-11-24 | 156 | 0.015693 |
| 2023-03-26 | 156 | 0.015693 |
| 2022-10-01 | 156 | 0.015693 |
| 2022-09-17 | 156 | 0.015693 |
| 2021-09-17 | 155 | 0.015593 |
| 2022-01-18 | 155 | 0.015593 |
| 2021-04-27 | 155 | 0.015593 |
| 2021-04-21 | 155 | 0.015593 |
| 2023-04-25 | 155 | 0.015593 |
| 2021-06-12 | 155 | 0.015593 |
| 2021-01-26 | 155 | 0.015593 |
| 2021-12-08 | 155 | 0.015593 |
| 2022-11-06 | 154 | 0.015492 |
| 2022-01-21 | 154 | 0.015492 |
| 2021-07-09 | 154 | 0.015492 |
| 2022-02-23 | 154 | 0.015492 |
| 2022-10-09 | 154 | 0.015492 |
| 2022-12-06 | 153 | 0.015391 |
| 2021-05-15 | 152 | 0.015291 |
| 2022-06-30 | 152 | 0.015291 |
| 2020-11-28 | 152 | 0.015291 |
| 2022-01-20 | 152 | 0.015291 |
| 2021-10-08 | 152 | 0.015291 |
| 2023-01-17 | 152 | 0.015291 |
| 2021-07-23 | 151 | 0.015190 |
| 2022-02-10 | 151 | 0.015190 |
| 2022-10-22 | 151 | 0.015190 |
| 2023-03-14 | 150 | 0.015090 |
| 2023-05-23 | 150 | 0.015090 |
| 2023-01-24 | 150 | 0.015090 |
| 2021-09-29 | 150 | 0.015090 |
| 2022-05-03 | 150 | 0.015090 |
| 2021-09-10 | 150 | 0.015090 |
| 2021-03-30 | 149 | 0.014989 |
| 2023-03-18 | 149 | 0.014989 |
| 2022-10-29 | 148 | 0.014888 |
| 2022-06-11 | 148 | 0.014888 |
| 2022-03-26 | 148 | 0.014888 |
| 2022-03-30 | 148 | 0.014888 |
| 2021-05-22 | 148 | 0.014888 |
| 2021-04-09 | 148 | 0.014888 |
| 2020-10-31 | 148 | 0.014888 |
| 2021-07-16 | 148 | 0.014888 |
| 2022-03-19 | 148 | 0.014888 |
| 2022-10-19 | 147 | 0.014788 |
| 2021-04-28 | 147 | 0.014788 |
| 2020-10-01 | 147 | 0.014788 |
| 2022-06-22 | 147 | 0.014788 |
| 2021-05-01 | 147 | 0.014788 |
| 2021-11-20 | 147 | 0.014788 |
| 2022-10-11 | 147 | 0.014788 |
| 2022-09-29 | 146 | 0.014687 |
| 2022-05-31 | 146 | 0.014687 |
| 2023-07-08 | 146 | 0.014687 |
| 2021-03-19 | 146 | 0.014687 |
| 2022-06-16 | 146 | 0.014687 |
| 2021-03-12 | 146 | 0.014687 |
| 2021-04-20 | 145 | 0.014587 |
| 2022-07-16 | 145 | 0.014587 |
| 2022-01-13 | 145 | 0.014587 |
| 2021-07-30 | 144 | 0.014486 |
| 2022-01-10 | 144 | 0.014486 |
| 2022-12-30 | 144 | 0.014486 |
| 2022-05-08 | 144 | 0.014486 |
| 2023-05-30 | 144 | 0.014486 |
| 2021-06-11 | 144 | 0.014486 |
| 2021-04-30 | 144 | 0.014486 |
| 2022-03-07 | 144 | 0.014486 |
| 2022-09-16 | 143 | 0.014385 |
| 2023-03-03 | 143 | 0.014385 |
| 2023-06-14 | 143 | 0.014385 |
| 2021-01-23 | 143 | 0.014385 |
| 2021-06-24 | 143 | 0.014385 |
| 2022-12-29 | 142 | 0.014285 |
| 2020-11-10 | 142 | 0.014285 |
| 2023-04-20 | 142 | 0.014285 |
| 2023-03-05 | 142 | 0.014285 |
| 2022-05-21 | 142 | 0.014285 |
| 2021-07-31 | 141 | 0.014184 |
| 2021-10-30 | 141 | 0.014184 |
| 2021-11-23 | 141 | 0.014184 |
| 2023-03-21 | 141 | 0.014184 |
| 2020-11-14 | 141 | 0.014184 |
| 2021-10-28 | 141 | 0.014184 |
| 2021-09-18 | 140 | 0.014084 |
| 2022-05-24 | 140 | 0.014084 |
| 2023-03-15 | 140 | 0.014084 |
| 2021-11-10 | 140 | 0.014084 |
| 2022-03-03 | 140 | 0.014084 |
| 2021-12-10 | 140 | 0.014084 |
| 2020-11-19 | 140 | 0.014084 |
| 2021-12-05 | 140 | 0.014084 |
| 2022-08-11 | 139 | 0.013983 |
| 2020-12-08 | 139 | 0.013983 |
| 2021-05-26 | 138 | 0.013882 |
| 2022-02-22 | 138 | 0.013882 |
| 2022-02-08 | 138 | 0.013882 |
| 2021-06-30 | 138 | 0.013882 |
| 2021-05-11 | 138 | 0.013882 |
| 2020-11-21 | 138 | 0.013882 |
| 2021-05-18 | 137 | 0.013782 |
| 2022-07-20 | 137 | 0.013782 |
| 2021-02-20 | 137 | 0.013782 |
| 2021-12-24 | 137 | 0.013782 |
| 2021-01-16 | 137 | 0.013782 |
| 2021-11-06 | 137 | 0.013782 |
| 2021-09-28 | 137 | 0.013782 |
| 2021-12-11 | 137 | 0.013782 |
| 2021-05-05 | 136 | 0.013681 |
| 2023-06-20 | 136 | 0.013681 |
| 2023-03-07 | 136 | 0.013681 |
| 2022-07-23 | 136 | 0.013681 |
| 2022-02-12 | 136 | 0.013681 |
| 2022-01-12 | 136 | 0.013681 |
| 2022-09-27 | 136 | 0.013681 |
| 2020-12-16 | 136 | 0.013681 |
| 2021-01-29 | 136 | 0.013681 |
| 2020-12-01 | 135 | 0.013581 |
| 2020-10-27 | 135 | 0.013581 |
| 2023-03-11 | 135 | 0.013581 |
| 2020-12-18 | 135 | 0.013581 |
| 2020-12-12 | 135 | 0.013581 |
| 2023-04-01 | 135 | 0.013581 |
| 2022-02-25 | 135 | 0.013581 |
| 2023-03-22 | 135 | 0.013581 |
| 2021-10-20 | 135 | 0.013581 |
| 2021-03-26 | 135 | 0.013581 |
| 2022-04-13 | 135 | 0.013581 |
| 2020-12-29 | 135 | 0.013581 |
| 2022-09-24 | 134 | 0.013480 |
| 2021-01-09 | 134 | 0.013480 |
| 2022-04-26 | 134 | 0.013480 |
| 2022-04-27 | 134 | 0.013480 |
| 2021-07-10 | 134 | 0.013480 |
| 2023-05-16 | 134 | 0.013480 |
| 2021-03-24 | 133 | 0.013379 |
| 2023-05-17 | 133 | 0.013379 |
| 2021-10-04 | 133 | 0.013379 |
| 2021-03-13 | 133 | 0.013379 |
| 2021-10-27 | 133 | 0.013379 |
| 2021-02-27 | 133 | 0.013379 |
| 2022-08-04 | 133 | 0.013379 |
| 2023-01-18 | 132 | 0.013279 |
| 2021-10-12 | 132 | 0.013279 |
| 2021-09-08 | 132 | 0.013279 |
| 2021-12-25 | 132 | 0.013279 |
| 2021-07-14 | 131 | 0.013178 |
| 2021-02-13 | 131 | 0.013178 |
| 2021-04-14 | 130 | 0.013078 |
| 2021-09-25 | 130 | 0.013078 |
| 2021-08-04 | 130 | 0.013078 |
| 2021-06-23 | 130 | 0.013078 |
| 2020-12-24 | 130 | 0.013078 |
| 2022-11-03 | 130 | 0.013078 |
| 2022-02-01 | 129 | 0.012977 |
| 2022-09-10 | 129 | 0.012977 |
| 2021-03-17 | 129 | 0.012977 |
| 2020-10-17 | 128 | 0.012876 |
| 2020-11-04 | 128 | 0.012876 |
| 2022-12-24 | 128 | 0.012876 |
| 2022-05-26 | 127 | 0.012776 |
| 2021-06-16 | 127 | 0.012776 |
| 2022-12-28 | 127 | 0.012776 |
| 2020-11-24 | 127 | 0.012776 |
| 2021-10-05 | 127 | 0.012776 |
| 2020-11-26 | 127 | 0.012776 |
| 2021-12-29 | 127 | 0.012776 |
| 2022-04-15 | 126 | 0.012675 |
| 2023-06-10 | 126 | 0.012675 |
| 2021-05-25 | 126 | 0.012675 |
| 2020-11-25 | 126 | 0.012675 |
| 2022-06-14 | 126 | 0.012675 |
| 2021-10-06 | 125 | 0.012575 |
| 2022-07-29 | 125 | 0.012575 |
| 2022-01-29 | 125 | 0.012575 |
| 2023-06-27 | 125 | 0.012575 |
| 2022-09-09 | 124 | 0.012474 |
| 2021-05-04 | 124 | 0.012474 |
| 2021-06-15 | 124 | 0.012474 |
| 2023-03-28 | 124 | 0.012474 |
| 2021-01-19 | 124 | 0.012474 |
| 2023-01-11 | 123 | 0.012373 |
| 2022-09-22 | 123 | 0.012373 |
| 2021-07-28 | 123 | 0.012373 |
| 2023-06-13 | 123 | 0.012373 |
| 2023-04-13 | 123 | 0.012373 |
| 2022-04-03 | 123 | 0.012373 |
| 2022-09-14 | 122 | 0.012273 |
| 2021-04-24 | 122 | 0.012273 |
| 2022-06-21 | 122 | 0.012273 |
| 2020-10-28 | 122 | 0.012273 |
| 2023-04-29 | 122 | 0.012273 |
| 2022-12-08 | 121 | 0.012172 |
| 2021-03-31 | 121 | 0.012172 |
| 2021-05-19 | 121 | 0.012172 |
| 2022-08-05 | 121 | 0.012172 |
| 2021-12-30 | 121 | 0.012172 |
| 2021-02-09 | 121 | 0.012172 |
| 2022-07-30 | 121 | 0.012172 |
| 2022-04-23 | 120 | 0.012072 |
| 2021-09-11 | 120 | 0.012072 |
| 2023-05-04 | 120 | 0.012072 |
| 2022-07-12 | 120 | 0.012072 |
| 2021-11-09 | 119 | 0.011971 |
| 2022-02-19 | 119 | 0.011971 |
| 2023-05-20 | 119 | 0.011971 |
| 2020-12-23 | 119 | 0.011971 |
| 2023-07-01 | 119 | 0.011971 |
| 2022-01-26 | 118 | 0.011871 |
| 2023-05-06 | 117 | 0.011770 |
| 2023-01-10 | 117 | 0.011770 |
| 2021-01-15 | 117 | 0.011770 |
| 2022-04-07 | 117 | 0.011770 |
| 2020-11-12 | 116 | 0.011669 |
| 2021-04-13 | 116 | 0.011669 |
| 2021-02-12 | 115 | 0.011569 |
| 2022-01-22 | 115 | 0.011569 |
| 2021-01-31 | 114 | 0.011468 |
| 2022-01-08 | 114 | 0.011468 |
| 2022-04-30 | 114 | 0.011468 |
| 2021-03-16 | 114 | 0.011468 |
| 2020-10-08 | 113 | 0.011368 |
| 2020-10-23 | 113 | 0.011368 |
| 2022-01-25 | 113 | 0.011368 |
| 2023-05-10 | 113 | 0.011368 |
| 2021-07-21 | 113 | 0.011368 |
| 2021-11-26 | 113 | 0.011368 |
| 2023-04-15 | 113 | 0.011368 |
| 2021-07-27 | 112 | 0.011267 |
| 2020-11-18 | 112 | 0.011267 |
| 2021-03-09 | 112 | 0.011267 |
| 2023-05-03 | 112 | 0.011267 |
| 2022-02-15 | 112 | 0.011267 |
| 2022-12-13 | 111 | 0.011166 |
| 2022-02-09 | 111 | 0.011166 |
| 2021-02-19 | 111 | 0.011166 |
| 2022-04-21 | 111 | 0.011166 |
| 2021-09-03 | 111 | 0.011166 |
| 2022-11-05 | 110 | 0.011066 |
| 2022-06-18 | 110 | 0.011066 |
| 2021-06-26 | 110 | 0.011066 |
| 2021-01-22 | 110 | 0.011066 |
| 2022-07-03 | 110 | 0.011066 |
| 2023-01-05 | 109 | 0.010965 |
| 2022-07-19 | 109 | 0.010965 |
| 2020-11-11 | 109 | 0.010965 |
| 2022-12-31 | 109 | 0.010965 |
| 2023-04-18 | 108 | 0.010865 |
| 2023-04-12 | 108 | 0.010865 |
| 2022-08-10 | 106 | 0.010663 |
| 2021-07-13 | 106 | 0.010663 |
| 2021-08-11 | 106 | 0.010663 |
| 2021-09-07 | 105 | 0.010563 |
| 2021-07-24 | 105 | 0.010563 |
| 2022-06-07 | 105 | 0.010563 |
| 2022-06-03 | 105 | 0.010563 |
| 2021-01-08 | 104 | 0.010462 |
| 2021-01-27 | 104 | 0.010462 |
| 2021-03-01 | 103 | 0.010362 |
| 2021-08-10 | 102 | 0.010261 |
| 2020-12-31 | 102 | 0.010261 |
| 2020-10-21 | 101 | 0.010160 |
| 2022-07-27 | 101 | 0.010160 |
| 2021-10-14 | 100 | 0.010060 |
| 2021-07-17 | 100 | 0.010060 |
| 2022-08-12 | 99 | 0.009959 |
| 2022-06-25 | 97 | 0.009758 |
| 2023-06-07 | 97 | 0.009758 |
| 2022-09-20 | 97 | 0.009758 |
| 2021-12-31 | 97 | 0.009758 |
| 2021-07-20 | 96 | 0.009657 |
| 2021-03-20 | 96 | 0.009657 |
| 2022-04-20 | 95 | 0.009557 |
| 2021-04-10 | 94 | 0.009456 |
| 2021-07-07 | 94 | 0.009456 |
| 2022-07-26 | 93 | 0.009356 |
| 2020-12-05 | 93 | 0.009356 |
| 2021-01-01 | 93 | 0.009356 |
| 2021-12-28 | 92 | 0.009255 |
| 2022-08-03 | 92 | 0.009255 |
| 2020-10-09 | 90 | 0.009054 |
| 2021-04-07 | 90 | 0.009054 |
| 2021-11-03 | 89 | 0.008953 |
| 2021-03-10 | 89 | 0.008953 |
| 2022-01-11 | 88 | 0.008853 |
| 2023-03-04 | 88 | 0.008853 |
| 2022-05-04 | 87 | 0.008752 |
| 2021-09-14 | 86 | 0.008651 |
| 2020-07-16 | 85 | 0.008551 |
| 2020-11-05 | 80 | 0.008048 |
| 2021-03-23 | 78 | 0.007847 |
| 2022-09-13 | 76 | 0.007645 |
| 2021-01-12 | 76 | 0.007645 |
| 2022-08-13 | 75 | 0.007545 |
| 2022-10-03 | 75 | 0.007545 |
| 2022-06-28 | 75 | 0.007545 |
| 2022-04-05 | 74 | 0.007444 |
| 2021-08-18 | 74 | 0.007444 |
| 2022-01-14 | 73 | 0.007344 |
| 2020-12-06 | 73 | 0.007344 |
| 2022-08-17 | 72 | 0.007243 |
| 2021-04-03 | 72 | 0.007243 |
| 2020-10-04 | 71 | 0.007142 |
| 2020-12-21 | 70 | 0.007042 |
| 2022-08-09 | 69 | 0.006941 |
| 2021-08-06 | 69 | 0.006941 |
| 2021-08-01 | 68 | 0.006841 |
| 2020-10-25 | 67 | 0.006740 |
| 2021-12-04 | 67 | 0.006740 |
| 2022-01-01 | 66 | 0.006639 |
| 2023-04-11 | 66 | 0.006639 |
| 2022-03-20 | 66 | 0.006639 |
| 2022-03-14 | 66 | 0.006639 |
| 2020-12-10 | 64 | 0.006438 |
| 2021-01-13 | 63 | 0.006338 |
| 2022-03-13 | 60 | 0.006036 |
| 2022-12-27 | 59 | 0.005935 |
| 2021-08-13 | 59 | 0.005935 |
| 2021-09-01 | 58 | 0.005835 |
| 2021-09-21 | 57 | 0.005734 |
| 2021-08-19 | 56 | 0.005633 |
| 2022-04-19 | 55 | 0.005533 |
| 2020-09-27 | 55 | 0.005533 |
| 2020-09-15 | 54 | 0.005432 |
| 2021-11-05 | 52 | 0.005231 |
| 2023-06-04 | 50 | 0.005030 |
| 2022-08-31 | 49 | 0.004929 |
| 2021-04-06 | 47 | 0.004728 |
| 2021-08-26 | 47 | 0.004728 |
| 2022-01-30 | 46 | 0.004627 |
| 2022-05-25 | 46 | 0.004627 |
| 2022-08-24 | 46 | 0.004627 |
| 2021-01-07 | 46 | 0.004627 |
| 2020-11-08 | 45 | 0.004527 |
| 2021-08-31 | 44 | 0.004426 |
| 2021-03-05 | 42 | 0.004225 |
| 2021-08-25 | 42 | 0.004225 |
| 2021-08-12 | 41 | 0.004124 |
| 2021-08-17 | 40 | 0.004024 |
| 2023-02-13 | 40 | 0.004024 |
| 2022-07-05 | 39 | 0.003923 |
| 2021-08-28 | 37 | 0.003722 |
| 2021-08-24 | 35 | 0.003521 |
| 2021-08-14 | 35 | 0.003521 |
| 2021-08-20 | 33 | 0.003320 |
| 2020-09-26 | 32 | 0.003219 |
| 2022-11-04 | 32 | 0.003219 |
| 2021-03-03 | 32 | 0.003219 |
| 2021-11-04 | 29 | 0.002917 |
| 2022-02-28 | 29 | 0.002917 |
| 2023-03-29 | 28 | 0.002817 |
| 2021-01-05 | 27 | 0.002716 |
| 2020-10-05 | 25 | 0.002515 |
| 2022-09-21 | 25 | 0.002515 |
| 2023-06-25 | 24 | 0.002414 |
| 2021-08-27 | 24 | 0.002414 |
| 2021-08-21 | 24 | 0.002414 |
| 2021-02-01 | 23 | 0.002314 |
| 2022-01-03 | 23 | 0.002314 |
| 2022-08-18 | 23 | 0.002314 |
| 2022-08-27 | 21 | 0.002113 |
| 2020-12-20 | 21 | 0.002113 |
| 2022-03-28 | 20 | 0.002012 |
| 2021-12-26 | 20 | 0.002012 |
| 2022-11-20 | 20 | 0.002012 |
| 2022-08-25 | 20 | 0.002012 |
| 2020-11-07 | 20 | 0.002012 |
| 2022-03-21 | 20 | 0.002012 |
| 2020-10-07 | 19 | 0.001911 |
| 2020-12-09 | 19 | 0.001911 |
| 2023-02-04 | 19 | 0.001911 |
| 2021-11-02 | 18 | 0.001811 |
| 2022-08-19 | 18 | 0.001811 |
| 2022-08-30 | 18 | 0.001811 |
| 2020-11-01 | 18 | 0.001811 |
| 2022-09-01 | 18 | 0.001811 |
| 2021-05-07 | 18 | 0.001811 |
| 2022-07-06 | 18 | 0.001811 |
| 2022-10-05 | 17 | 0.001710 |
| 2023-02-20 | 16 | 0.001610 |
| 2021-07-04 | 16 | 0.001610 |
| 2022-12-18 | 16 | 0.001610 |
| 2021-01-24 | 16 | 0.001610 |
| 2021-12-20 | 16 | 0.001610 |
| 2021-01-30 | 16 | 0.001610 |
| 2023-01-01 | 16 | 0.001610 |
| 2022-01-24 | 16 | 0.001610 |
| 2023-02-19 | 16 | 0.001610 |
| 2022-08-23 | 15 | 0.001509 |
| 2020-11-09 | 15 | 0.001509 |
| 2022-12-19 | 15 | 0.001509 |
| 2021-02-22 | 15 | 0.001509 |
| 2022-02-07 | 15 | 0.001509 |
| 2021-12-07 | 15 | 0.001509 |
| 2022-11-27 | 15 | 0.001509 |
| 2022-05-05 | 14 | 0.001408 |
| 2021-04-26 | 14 | 0.001408 |
| 2021-01-11 | 14 | 0.001408 |
| 2021-02-07 | 14 | 0.001408 |
| 2022-12-05 | 14 | 0.001408 |
| 2022-04-16 | 13 | 0.001308 |
| 2021-03-29 | 13 | 0.001308 |
| 2022-09-05 | 13 | 0.001308 |
| 2022-02-27 | 12 | 0.001207 |
| 2022-08-20 | 12 | 0.001207 |
| 2022-12-09 | 12 | 0.001207 |
| 2021-04-04 | 12 | 0.001207 |
| 2021-12-27 | 12 | 0.001207 |
| 2020-04-11 | 12 | 0.001207 |
| 2020-12-07 | 12 | 0.001207 |
| 2022-02-04 | 12 | 0.001207 |
| 2021-08-09 | 12 | 0.001207 |
| 2021-01-17 | 12 | 0.001207 |
| 2021-01-25 | 12 | 0.001207 |
| 2020-11-03 | 11 | 0.001107 |
| 2020-12-26 | 11 | 0.001107 |
| 2022-08-26 | 11 | 0.001107 |
| 2021-05-10 | 11 | 0.001107 |
| 2021-01-10 | 11 | 0.001107 |
| 2022-12-07 | 11 | 0.001107 |
| 2020-10-26 | 11 | 0.001107 |
| 2022-01-17 | 11 | 0.001107 |
| 2021-08-16 | 10 | 0.001006 |
| 2022-11-21 | 10 | 0.001006 |
| 2020-12-28 | 10 | 0.001006 |
| 2021-06-14 | 10 | 0.001006 |
| 2022-10-31 | 10 | 0.001006 |
| 2021-03-07 | 10 | 0.001006 |
| 2020-11-29 | 10 | 0.001006 |
| 2020-12-27 | 10 | 0.001006 |
| 2022-12-25 | 10 | 0.001006 |
| 2021-02-08 | 10 | 0.001006 |
| 2020-10-02 | 10 | 0.001006 |
| 2021-02-21 | 10 | 0.001006 |
| 2021-05-24 | 10 | 0.001006 |
| 2021-06-07 | 10 | 0.001006 |
| 2021-06-06 | 10 | 0.001006 |
| 2020-11-23 | 10 | 0.001006 |
| 2022-01-23 | 10 | 0.001006 |
| 2022-01-31 | 10 | 0.001006 |
| 2022-04-11 | 10 | 0.001006 |
| 2021-08-22 | 10 | 0.001006 |
| 2020-11-30 | 10 | 0.001006 |
| 2020-10-18 | 10 | 0.001006 |
| 2023-01-30 | 9 | 0.000905 |
| 2021-06-20 | 9 | 0.000905 |
| 2021-06-27 | 9 | 0.000905 |
| 2020-12-13 | 9 | 0.000905 |
| 2022-12-26 | 9 | 0.000905 |
| 2020-11-16 | 9 | 0.000905 |
| 2022-08-16 | 9 | 0.000905 |
| 2021-02-14 | 9 | 0.000905 |
| 2021-12-12 | 8 | 0.000805 |
| 2022-12-11 | 8 | 0.000805 |
| 2020-11-22 | 8 | 0.000805 |
| 2023-05-02 | 8 | 0.000805 |
| 2023-06-12 | 8 | 0.000805 |
| 2022-05-15 | 8 | 0.000805 |
| 2022-02-13 | 8 | 0.000805 |
| 2022-07-17 | 8 | 0.000805 |
| 2023-01-08 | 8 | 0.000805 |
| 2021-11-29 | 8 | 0.000805 |
| 2023-01-23 | 8 | 0.000805 |
| 2022-01-16 | 8 | 0.000805 |
| 2023-04-08 | 8 | 0.000805 |
| 2021-09-13 | 8 | 0.000805 |
| 2021-04-19 | 8 | 0.000805 |
| 2021-03-08 | 8 | 0.000805 |
| 2021-02-15 | 8 | 0.000805 |
| 2021-05-03 | 8 | 0.000805 |
| 2021-04-25 | 8 | 0.000805 |
| 2021-08-08 | 8 | 0.000805 |
| 2022-05-01 | 8 | 0.000805 |
| 2021-09-12 | 8 | 0.000805 |
| 2021-04-12 | 8 | 0.000805 |
| 2021-04-18 | 7 | 0.000704 |
| 2021-03-22 | 7 | 0.000704 |
| 2021-05-31 | 7 | 0.000704 |
| 2023-04-30 | 7 | 0.000704 |
| 2023-02-27 | 7 | 0.000704 |
| 2021-08-05 | 7 | 0.000704 |
| 2021-10-31 | 7 | 0.000704 |
| 2023-04-09 | 7 | 0.000704 |
| 2021-03-15 | 7 | 0.000704 |
| 2022-04-04 | 7 | 0.000704 |
| 2021-04-05 | 7 | 0.000704 |
| 2021-12-09 | 7 | 0.000704 |
| 2021-11-15 | 7 | 0.000704 |
| 2022-06-19 | 7 | 0.000704 |
| 2021-10-10 | 7 | 0.000704 |
| 2021-08-23 | 7 | 0.000704 |
| 2022-11-13 | 7 | 0.000704 |
| 2021-06-21 | 7 | 0.000704 |
| 2022-10-30 | 7 | 0.000704 |
| 2021-02-28 | 7 | 0.000704 |
| 2021-01-18 | 7 | 0.000704 |
| 2021-10-17 | 7 | 0.000704 |
| 2022-06-26 | 7 | 0.000704 |
| 2023-04-03 | 7 | 0.000704 |
| 2021-11-01 | 6 | 0.000604 |
| 2023-01-15 | 6 | 0.000604 |
| 2022-11-07 | 6 | 0.000604 |
| 2021-05-17 | 6 | 0.000604 |
| 2022-06-06 | 6 | 0.000604 |
| 2021-12-13 | 6 | 0.000604 |
| 2021-04-11 | 6 | 0.000604 |
| 2022-05-23 | 6 | 0.000604 |
| 2021-11-22 | 6 | 0.000604 |
| 2022-04-18 | 6 | 0.000604 |
| 2021-08-15 | 6 | 0.000604 |
| 2023-03-13 | 6 | 0.000604 |
| 2023-01-09 | 6 | 0.000604 |
| 2021-11-14 | 6 | 0.000604 |
| 2022-05-16 | 6 | 0.000604 |
| 2022-07-18 | 6 | 0.000604 |
| 2020-11-15 | 6 | 0.000604 |
| 2021-03-21 | 6 | 0.000604 |
| 2023-07-09 | 6 | 0.000604 |
| 2021-10-13 | 6 | 0.000604 |
| 2023-06-05 | 6 | 0.000604 |
| 2022-02-21 | 6 | 0.000604 |
| 2021-10-25 | 6 | 0.000604 |
| 2021-07-11 | 5 | 0.000503 |
| 2021-11-21 | 5 | 0.000503 |
| 2022-12-12 | 5 | 0.000503 |
| 2023-07-03 | 5 | 0.000503 |
| 2022-06-13 | 5 | 0.000503 |
| 2021-09-26 | 5 | 0.000503 |
| 2022-08-08 | 5 | 0.000503 |
| 2021-06-13 | 5 | 0.000503 |
| 2023-06-19 | 5 | 0.000503 |
| 2022-05-09 | 5 | 0.000503 |
| 2023-04-23 | 5 | 0.000503 |
| 2023-05-22 | 5 | 0.000503 |
| 2021-06-28 | 5 | 0.000503 |
| 2020-10-13 | 5 | 0.000503 |
| 2023-04-17 | 5 | 0.000503 |
| 2021-10-18 | 5 | 0.000503 |
| 2021-05-14 | 5 | 0.000503 |
| 2023-02-26 | 5 | 0.000503 |
| 2023-01-29 | 5 | 0.000503 |
| 2020-09-28 | 5 | 0.000503 |
| 2021-08-29 | 4 | 0.000402 |
| 2021-05-23 | 4 | 0.000402 |
| 2021-07-25 | 4 | 0.000402 |
| 2020-10-19 | 4 | 0.000402 |
| 2021-08-30 | 4 | 0.000402 |
| 2022-06-20 | 4 | 0.000402 |
| 2022-02-20 | 4 | 0.000402 |
| 2022-02-14 | 4 | 0.000402 |
| 2022-05-22 | 4 | 0.000402 |
| 2021-07-12 | 4 | 0.000402 |
| 2021-05-09 | 4 | 0.000402 |
| 2021-07-05 | 4 | 0.000402 |
| 2022-06-12 | 4 | 0.000402 |
| 2021-07-19 | 4 | 0.000402 |
| 2021-05-30 | 4 | 0.000402 |
| 2022-07-25 | 4 | 0.000402 |
| 2022-09-19 | 4 | 0.000402 |
| 2022-09-11 | 4 | 0.000402 |
| 2023-05-15 | 4 | 0.000402 |
| 2023-07-02 | 4 | 0.000402 |
| 2023-01-16 | 4 | 0.000402 |
| 2022-07-31 | 4 | 0.000402 |
| 2021-09-27 | 4 | 0.000402 |
| 2021-10-11 | 4 | 0.000402 |
| 2022-04-10 | 4 | 0.000402 |
| 2021-10-24 | 4 | 0.000402 |
| 2023-05-28 | 4 | 0.000402 |
| 2022-09-12 | 4 | 0.000402 |
| 2022-08-15 | 4 | 0.000402 |
| 2023-03-12 | 4 | 0.000402 |
| 2022-09-25 | 4 | 0.000402 |
| 2022-07-24 | 4 | 0.000402 |
| 2023-03-27 | 4 | 0.000402 |
| 2022-08-29 | 4 | 0.000402 |
| 2022-10-17 | 4 | 0.000402 |
| 2022-10-24 | 4 | 0.000402 |
| 2023-03-20 | 3 | 0.000302 |
| 2022-05-29 | 3 | 0.000302 |
| 2023-05-01 | 3 | 0.000302 |
| 2022-09-26 | 3 | 0.000302 |
| 2022-04-25 | 3 | 0.000302 |
| 2022-04-17 | 3 | 0.000302 |
| 2021-09-05 | 3 | 0.000302 |
| 2022-11-28 | 3 | 0.000302 |
| 2023-03-19 | 3 | 0.000302 |
| 2022-10-23 | 3 | 0.000302 |
| 2023-06-26 | 3 | 0.000302 |
| 2021-09-06 | 3 | 0.000302 |
| 2022-08-14 | 3 | 0.000302 |
| 2023-04-24 | 3 | 0.000302 |
| 2021-12-06 | 3 | 0.000302 |
| 2021-11-28 | 3 | 0.000302 |
| 2021-03-14 | 3 | 0.000302 |
| 2023-06-18 | 2 | 0.000201 |
| 2022-07-04 | 2 | 0.000201 |
| 2021-07-18 | 2 | 0.000201 |
| 2021-07-26 | 2 | 0.000201 |
| 2022-05-02 | 2 | 0.000201 |
| 2021-11-08 | 2 | 0.000201 |
| 2020-10-11 | 2 | 0.000201 |
| 2021-09-19 | 2 | 0.000201 |
| 2022-08-01 | 2 | 0.000201 |
| 2023-04-10 | 2 | 0.000201 |
| 2023-05-29 | 2 | 0.000201 |
| 2022-08-21 | 2 | 0.000201 |
| 2022-08-22 | 2 | 0.000201 |
| 2023-01-22 | 2 | 0.000201 |
| 2022-05-30 | 2 | 0.000201 |
| 2021-09-20 | 2 | 0.000201 |
| 2021-07-06 | 2 | 0.000201 |
| 2022-10-16 | 2 | 0.000201 |
| 2023-04-16 | 2 | 0.000201 |
| 2022-11-02 | 1 | 0.000101 |
| 2022-01-04 | 1 | 0.000101 |
| 2022-10-13 | 1 | 0.000101 |
| 2022-08-28 | 1 | 0.000101 |
| 2022-11-14 | 1 | 0.000101 |
| 2021-10-02 | 1 | 0.000101 |
| 2022-04-24 | 1 | 0.000101 |
| 2022-05-18 | 1 | 0.000101 |
| 2022-09-18 | 1 | 0.000101 |
| 2023-06-11 | 1 | 0.000101 |
| 2023-05-14 | 1 | 0.000101 |
| 2023-05-21 | 1 | 0.000101 |
| 2022-06-27 | 1 | 0.000101 |
| 2023-07-04 | 1 | 0.000101 |
| 2021-08-03 | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_datefirstdonation__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstdonation__c es 727687. Lo que supone un 73.20323439939482% El nº de vacios para la variable msf_datefirstdonation__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2017-12-01 | 4910 | 1.843252 |
| 2010-02-01 | 4854 | 1.822229 |
| 2020-07-01 | 4209 | 1.580091 |
| 2014-11-01 | 3026 | 1.135984 |
| 2003-08-01 | 2919 | 1.095815 |
| ... | ... | ... |
| 1994-06-28 | 1 | 0.000375 |
| 2008-08-26 | 1 | 0.000375 |
| 2002-02-09 | 1 | 0.000375 |
| 2011-03-31 | 1 | 0.000375 |
| 2012-02-05 | 1 | 0.000375 |
9586 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_datefirstrecurringdonorquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datefirstrecurringdonorquota__c es 48715. Lo que supone un 4.9005899016562315% El nº de vacios para la variable msf_datefirstrecurringdonorquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2006-01-05 | 9932 | 1.050617 |
| 2011-01-03 | 9921 | 1.049454 |
| 2009-01-02 | 9654 | 1.021210 |
| 2021-01-05 | 8946 | 0.946317 |
| 2014-11-03 | 8567 | 0.906226 |
| 2015-01-02 | 8523 | 0.901572 |
| 2005-02-04 | 8472 | 0.896177 |
| 2014-12-02 | 8309 | 0.878935 |
| 2005-01-04 | 8185 | 0.865818 |
| 2012-01-02 | 8034 | 0.849845 |
| 2016-07-01 | 7977 | 0.843815 |
| 2004-02-01 | 7780 | 0.822976 |
| 2004-01-01 | 7525 | 0.796002 |
| 2015-10-01 | 7445 | 0.787540 |
| 2007-01-04 | 6987 | 0.739092 |
| 2016-01-04 | 6778 | 0.716984 |
| 2017-04-03 | 6649 | 0.703338 |
| 2010-01-04 | 6590 | 0.697097 |
| 2003-01-01 | 6482 | 0.685673 |
| 2015-11-03 | 6357 | 0.672450 |
| 2013-01-02 | 6285 | 0.664834 |
| 2016-04-01 | 6213 | 0.657218 |
| 2015-12-02 | 6209 | 0.656794 |
| 2003-03-01 | 6125 | 0.647909 |
| 2017-06-01 | 6057 | 0.640716 |
| 2016-08-01 | 6024 | 0.637225 |
| 2015-03-02 | 5930 | 0.627282 |
| 2015-02-02 | 5913 | 0.625483 |
| 2014-05-05 | 5836 | 0.617338 |
| 2016-10-03 | 5733 | 0.606443 |
| 2016-12-01 | 5723 | 0.605385 |
| 2015-04-01 | 5646 | 0.597240 |
| 2015-05-04 | 5559 | 0.588037 |
| 2017-07-03 | 5550 | 0.587085 |
| 2016-05-02 | 5498 | 0.581584 |
| 2016-11-02 | 5485 | 0.580209 |
| 2017-12-04 | 5456 | 0.577141 |
| 2010-02-01 | 5447 | 0.576189 |
| 2016-06-01 | 5400 | 0.571218 |
| 2017-01-02 | 5374 | 0.568467 |
| 2015-06-02 | 5328 | 0.563601 |
| 2006-02-03 | 5327 | 0.563496 |
| 2016-03-01 | 5319 | 0.562649 |
| 2017-08-01 | 5305 | 0.561168 |
| 2017-03-02 | 5278 | 0.558312 |
| 2009-02-03 | 5192 | 0.549215 |
| 2016-02-01 | 5139 | 0.543609 |
| 2014-01-02 | 5138 | 0.543503 |
| 2015-08-03 | 5125 | 0.542128 |
| 2014-08-01 | 4955 | 0.524145 |
| 2017-05-02 | 4884 | 0.516635 |
| 2014-06-05 | 4880 | 0.516211 |
| 2015-07-01 | 4823 | 0.510182 |
| 2017-02-02 | 4793 | 0.507009 |
| 2018-01-03 | 4793 | 0.507009 |
| 2009-12-02 | 4788 | 0.506480 |
| 2018-02-01 | 4744 | 0.501825 |
| 2018-06-01 | 4697 | 0.496854 |
| 2010-12-02 | 4667 | 0.493680 |
| 2018-03-01 | 4629 | 0.489660 |
| 2014-10-02 | 4554 | 0.481727 |
| 2014-04-02 | 4549 | 0.481198 |
| 2000-02-01 | 4504 | 0.476438 |
| 2012-02-01 | 4426 | 0.468187 |
| 2007-02-05 | 4376 | 0.462898 |
| 2013-02-01 | 4356 | 0.460782 |
| 2017-09-01 | 4342 | 0.459301 |
| 2011-12-01 | 4336 | 0.458667 |
| 2014-07-02 | 4277 | 0.452426 |
| 2018-07-02 | 4214 | 0.445761 |
| 2017-11-02 | 4202 | 0.444492 |
| 2014-02-03 | 4167 | 0.440790 |
| 2022-04-02 | 4157 | 0.439732 |
| 2018-08-01 | 4146 | 0.438568 |
| 2018-12-03 | 4109 | 0.434654 |
| 2011-02-01 | 4044 | 0.427779 |
| 2017-10-02 | 4002 | 0.423336 |
| 2018-04-03 | 3975 | 0.420480 |
| 2018-11-02 | 3934 | 0.416143 |
| 2008-12-01 | 3868 | 0.409161 |
| 2013-12-02 | 3847 | 0.406940 |
| 2005-03-04 | 3824 | 0.404507 |
| 2013-11-04 | 3805 | 0.402497 |
| 2019-12-02 | 3789 | 0.400804 |
| 2008-01-03 | 3781 | 0.399958 |
| 2020-02-03 | 3769 | 0.398689 |
| 2000-01-01 | 3719 | 0.393400 |
| 2014-03-03 | 3718 | 0.393294 |
| 2019-01-02 | 3698 | 0.391178 |
| 1994-10-01 | 3689 | 0.390226 |
| 2008-02-04 | 3668 | 0.388005 |
| 2019-11-04 | 3644 | 0.385466 |
| 2016-09-01 | 3614 | 0.382293 |
| 2011-04-01 | 3614 | 0.382293 |
| 2006-12-02 | 3592 | 0.379965 |
| 2020-03-02 | 3576 | 0.378273 |
| 2019-06-03 | 3564 | 0.377004 |
| 2021-04-02 | 3550 | 0.375523 |
| 2021-03-02 | 3533 | 0.373724 |
| 2022-12-02 | 3531 | 0.373513 |
| 2020-01-02 | 3525 | 0.372878 |
| 2018-05-03 | 3520 | 0.372349 |
| 2013-05-02 | 3516 | 0.371926 |
| 2014-09-03 | 3514 | 0.371715 |
| 2021-07-02 | 3491 | 0.369282 |
| 2019-08-01 | 3489 | 0.369070 |
| 2013-08-02 | 3472 | 0.367272 |
| 2019-05-02 | 3458 | 0.365791 |
| 2019-04-01 | 3450 | 0.364945 |
| 2021-06-02 | 3422 | 0.361983 |
| 2019-07-01 | 3411 | 0.360819 |
| 2023-03-02 | 3388 | 0.358386 |
| 2022-07-05 | 3378 | 0.357328 |
| 2019-02-01 | 3371 | 0.356588 |
| 2013-06-03 | 3351 | 0.354472 |
| 2001-03-01 | 3338 | 0.353097 |
| 2011-03-01 | 3329 | 0.352145 |
| 1995-02-01 | 3309 | 0.350029 |
| 2023-04-04 | 3299 | 0.348972 |
| 2011-08-02 | 3284 | 0.347385 |
| 2018-10-02 | 3267 | 0.345587 |
| 2012-12-03 | 3224 | 0.341038 |
| 2023-06-02 | 3214 | 0.339980 |
| 2013-03-01 | 3173 | 0.335643 |
| 2019-03-01 | 3145 | 0.332681 |
| 2007-12-02 | 3138 | 0.331941 |
| 2013-04-02 | 3135 | 0.331624 |
| 2022-11-03 | 3133 | 0.331412 |
| 2019-10-02 | 3129 | 0.330989 |
| 2012-11-02 | 3101 | 0.328027 |
| 2015-09-01 | 3095 | 0.327392 |
| 2023-07-04 | 3090 | 0.326863 |
| 2022-06-02 | 3090 | 0.326863 |
| 2021-10-02 | 3081 | 0.325911 |
| 2013-07-01 | 3057 | 0.323373 |
| 2001-02-01 | 3052 | 0.322844 |
| 2021-12-02 | 3037 | 0.321257 |
| 2010-08-02 | 3034 | 0.320940 |
| 2021-05-04 | 3031 | 0.320622 |
| 2023-02-02 | 3003 | 0.317660 |
| 2004-03-01 | 2977 | 0.314910 |
| 2005-12-03 | 2966 | 0.313747 |
| 2022-10-04 | 2932 | 0.310150 |
| 2012-08-01 | 2918 | 0.308669 |
| 2021-11-03 | 2889 | 0.305601 |
| 2022-01-04 | 2796 | 0.295764 |
| 2022-08-02 | 2788 | 0.294918 |
| 1998-03-01 | 2785 | 0.294600 |
| 2010-03-01 | 2774 | 0.293437 |
| 2009-03-03 | 2766 | 0.292590 |
| 2011-11-02 | 2764 | 0.292379 |
| 2013-10-02 | 2744 | 0.290263 |
| 2018-09-03 | 2741 | 0.289946 |
| 2021-08-03 | 2733 | 0.289100 |
| 2023-01-03 | 2726 | 0.288359 |
| 2012-03-01 | 2704 | 0.286032 |
| 1999-01-01 | 2671 | 0.282541 |
| 2021-02-02 | 2665 | 0.281906 |
| 2022-03-02 | 2663 | 0.281695 |
| 1994-02-01 | 2653 | 0.280637 |
| 2012-06-04 | 2649 | 0.280214 |
| 2022-05-03 | 2647 | 0.280002 |
| 2012-04-02 | 2627 | 0.277887 |
| 2011-05-02 | 2597 | 0.274713 |
| 2013-09-02 | 2590 | 0.273973 |
| 2022-02-02 | 2538 | 0.268472 |
| 2008-08-08 | 2535 | 0.268155 |
| 2020-04-02 | 2480 | 0.262337 |
| 2002-01-01 | 2476 | 0.261914 |
| 2010-04-01 | 2454 | 0.259587 |
| 2012-07-02 | 2451 | 0.259269 |
| 2011-07-01 | 2432 | 0.257259 |
| 2006-11-03 | 2415 | 0.255461 |
| 2011-06-01 | 2406 | 0.254509 |
| 2010-07-01 | 2364 | 0.250066 |
| 2008-04-04 | 2363 | 0.249961 |
| 2023-05-03 | 2331 | 0.246576 |
| 2020-05-03 | 2326 | 0.246047 |
| 2007-04-02 | 2315 | 0.244883 |
| 2007-03-02 | 2312 | 0.244566 |
| 2011-09-02 | 2278 | 0.240969 |
| 2011-10-04 | 2247 | 0.237690 |
| 2002-12-01 | 2244 | 0.237373 |
| 1999-02-01 | 2212 | 0.233988 |
| 2012-05-03 | 2203 | 0.233036 |
| 2008-03-03 | 2103 | 0.222458 |
| 2006-03-03 | 2090 | 0.221082 |
| 2009-04-02 | 2086 | 0.220659 |
| 2009-07-02 | 2080 | 0.220025 |
| 2012-10-01 | 2029 | 0.214630 |
| 2008-06-02 | 2025 | 0.214207 |
| 2010-06-02 | 2020 | 0.213678 |
| 2004-12-05 | 2000 | 0.211562 |
| 1996-02-01 | 1978 | 0.209235 |
| 2006-04-03 | 1971 | 0.208494 |
| 2007-10-04 | 1948 | 0.206061 |
| 2019-09-02 | 1913 | 0.202359 |
| 2001-01-01 | 1885 | 0.199397 |
| 2007-05-04 | 1875 | 0.198339 |
| 2007-09-03 | 1874 | 0.198234 |
| 2010-10-04 | 1874 | 0.198234 |
| 2007-07-04 | 1851 | 0.195801 |
| 2020-08-03 | 1830 | 0.193579 |
| 2005-11-03 | 1805 | 0.190935 |
| 1994-07-01 | 1800 | 0.190406 |
| 2003-12-01 | 1795 | 0.189877 |
| 2010-05-03 | 1767 | 0.186915 |
| 2005-08-02 | 1758 | 0.185963 |
| 2010-11-02 | 1716 | 0.181520 |
| 2009-08-03 | 1714 | 0.181309 |
| 2009-06-04 | 1707 | 0.180568 |
| 2009-10-02 | 1699 | 0.179722 |
| 2008-07-04 | 1689 | 0.178664 |
| 2009-05-04 | 1670 | 0.176654 |
| 2005-06-03 | 1660 | 0.175597 |
| 2006-06-02 | 1649 | 0.174433 |
| 1997-02-01 | 1647 | 0.174221 |
| 2008-05-02 | 1629 | 0.172317 |
| 2020-06-02 | 1626 | 0.172000 |
| 2005-07-04 | 1625 | 0.171894 |
| 2007-08-02 | 1584 | 0.167557 |
| 1998-02-01 | 1546 | 0.163537 |
| 2006-07-03 | 1528 | 0.161633 |
| 2009-09-02 | 1514 | 0.160152 |
| 2008-09-01 | 1513 | 0.160047 |
| 2009-11-02 | 1506 | 0.159306 |
| 1995-04-01 | 1480 | 0.156556 |
| 2000-03-01 | 1436 | 0.151902 |
| 2007-11-02 | 1434 | 0.151690 |
| 1994-01-01 | 1422 | 0.150421 |
| 2012-09-03 | 1422 | 0.150421 |
| 2022-09-02 | 1403 | 0.148411 |
| 2021-09-02 | 1394 | 0.147459 |
| 2020-07-01 | 1384 | 0.146401 |
| 2005-04-04 | 1365 | 0.144391 |
| 1992-11-01 | 1352 | 0.143016 |
| 1998-01-01 | 1348 | 0.142593 |
| 1994-09-01 | 1327 | 0.140371 |
| 2008-10-02 | 1293 | 0.136775 |
| 2008-11-03 | 1292 | 0.136669 |
| 2010-09-02 | 1286 | 0.136034 |
| 2005-05-04 | 1242 | 0.131380 |
| 2007-06-05 | 1226 | 0.129688 |
| 2020-09-01 | 1223 | 0.129370 |
| 2005-09-02 | 1180 | 0.124822 |
| 1995-03-01 | 1169 | 0.123658 |
| 2006-08-02 | 1121 | 0.118581 |
| 1999-06-01 | 1076 | 0.113820 |
| 2005-10-03 | 1055 | 0.111599 |
| 2006-05-04 | 973 | 0.102925 |
| 1999-03-01 | 960 | 0.101550 |
| 1997-01-01 | 930 | 0.098376 |
| 2002-04-01 | 880 | 0.093087 |
| 2002-02-01 | 878 | 0.092876 |
| 2004-04-01 | 875 | 0.092558 |
| 1994-03-01 | 866 | 0.091606 |
| 2006-09-04 | 862 | 0.091183 |
| 2003-11-01 | 850 | 0.089914 |
| 1995-01-01 | 839 | 0.088750 |
| 2003-06-01 | 830 | 0.087798 |
| 2006-10-02 | 826 | 0.087375 |
| 1995-07-01 | 821 | 0.086846 |
| 2003-04-01 | 795 | 0.084096 |
| 2002-05-01 | 780 | 0.082509 |
| 2004-11-04 | 769 | 0.081346 |
| 2001-04-01 | 756 | 0.079970 |
| 2000-05-01 | 744 | 0.078701 |
| 1999-07-01 | 710 | 0.075105 |
| 1994-04-01 | 691 | 0.073095 |
| 2003-08-01 | 683 | 0.072248 |
| 2004-05-01 | 667 | 0.070556 |
| 2004-06-01 | 662 | 0.070027 |
| 1992-12-01 | 662 | 0.070027 |
| 1995-10-01 | 661 | 0.069921 |
| 1996-04-01 | 651 | 0.068863 |
| 1999-05-01 | 643 | 0.068017 |
| 2000-04-01 | 634 | 0.067065 |
| 2001-08-01 | 617 | 0.065267 |
| 2004-08-01 | 609 | 0.064421 |
| 1999-12-01 | 591 | 0.062517 |
| 2020-12-02 | 589 | 0.062305 |
| 1998-04-01 | 577 | 0.061036 |
| 1996-12-01 | 573 | 0.060613 |
| 2003-05-01 | 555 | 0.058708 |
| 1998-09-01 | 549 | 0.058074 |
| 1998-12-01 | 531 | 0.056170 |
| 1994-06-01 | 524 | 0.055429 |
| 2000-01-13 | 516 | 0.054583 |
| 2001-12-01 | 513 | 0.054266 |
| 1996-01-01 | 510 | 0.053948 |
| 2001-07-01 | 505 | 0.053419 |
| 2004-10-06 | 499 | 0.052785 |
| 1998-11-01 | 492 | 0.052044 |
| 2004-07-01 | 490 | 0.051833 |
| 1993-11-01 | 489 | 0.051727 |
| 2002-11-01 | 485 | 0.051304 |
| 1996-03-01 | 477 | 0.050458 |
| 1996-06-01 | 476 | 0.050352 |
| 1995-06-01 | 466 | 0.049294 |
| 2002-08-01 | 463 | 0.048977 |
| 2003-10-01 | 455 | 0.048130 |
| 1998-05-01 | 447 | 0.047284 |
| 1992-06-01 | 445 | 0.047073 |
| 1993-01-01 | 415 | 0.043899 |
| 1993-03-01 | 412 | 0.043582 |
| 1993-07-01 | 404 | 0.042736 |
| 2002-03-01 | 402 | 0.042524 |
| 1997-03-01 | 398 | 0.042101 |
| 1996-07-01 | 379 | 0.040091 |
| 1998-06-01 | 377 | 0.039879 |
| 2004-09-03 | 370 | 0.039139 |
| 1994-08-01 | 369 | 0.039033 |
| 2000-06-01 | 366 | 0.038716 |
| 1993-02-01 | 354 | 0.037446 |
| 1999-04-01 | 343 | 0.036283 |
| 2020-11-04 | 340 | 0.035966 |
| 1994-05-01 | 332 | 0.035119 |
| 2002-09-01 | 332 | 0.035119 |
| 1994-12-01 | 329 | 0.034802 |
| 2003-09-01 | 329 | 0.034802 |
| 1993-12-01 | 325 | 0.034379 |
| 2003-02-01 | 323 | 0.034167 |
| 2000-07-01 | 320 | 0.033850 |
| 1997-11-01 | 312 | 0.033004 |
| 1995-05-01 | 297 | 0.031417 |
| 2003-07-01 | 287 | 0.030359 |
| 2002-10-01 | 279 | 0.029513 |
| 1999-08-01 | 278 | 0.029407 |
| 1995-12-01 | 269 | 0.028455 |
| 2001-11-01 | 263 | 0.027820 |
| 1995-11-01 | 259 | 0.027397 |
| 1995-09-01 | 246 | 0.026022 |
| 1998-08-01 | 245 | 0.025916 |
| 1994-11-01 | 240 | 0.025387 |
| 2001-05-01 | 237 | 0.025070 |
| 2020-10-02 | 233 | 0.024647 |
| 1997-12-01 | 228 | 0.024118 |
| 1992-08-01 | 225 | 0.023801 |
| 1998-07-01 | 224 | 0.023695 |
| 1996-05-01 | 223 | 0.023589 |
| 1994-01-11 | 219 | 0.023166 |
| 1997-06-01 | 216 | 0.022849 |
| 1996-08-01 | 198 | 0.020945 |
| 2000-04-05 | 197 | 0.020839 |
| 1997-05-01 | 196 | 0.020733 |
| 2001-09-01 | 196 | 0.020733 |
| 1993-06-01 | 195 | 0.020627 |
| 1996-09-01 | 195 | 0.020627 |
| 2001-10-01 | 192 | 0.020310 |
| 1993-10-01 | 189 | 0.019993 |
| 1992-07-01 | 185 | 0.019569 |
| 1993-05-01 | 184 | 0.019464 |
| 2000-12-01 | 179 | 0.018935 |
| 1997-04-01 | 174 | 0.018406 |
| 1997-07-01 | 166 | 0.017560 |
| 2000-08-01 | 157 | 0.016608 |
| 1996-11-01 | 151 | 0.015973 |
| 1999-09-01 | 150 | 0.015867 |
| 1999-11-01 | 150 | 0.015867 |
| 1999-10-01 | 147 | 0.015550 |
| 2017-07-01 | 144 | 0.015232 |
| 2000-03-09 | 141 | 0.014915 |
| 2015-01-01 | 140 | 0.014809 |
| 2017-01-01 | 140 | 0.014809 |
| 2014-12-01 | 139 | 0.014704 |
| 1993-04-01 | 139 | 0.014704 |
| 1995-08-01 | 138 | 0.014598 |
| 2015-02-01 | 137 | 0.014492 |
| 2002-06-17 | 134 | 0.014175 |
| 2001-06-01 | 129 | 0.013646 |
| 2000-11-01 | 128 | 0.013540 |
| 1996-10-01 | 126 | 0.013328 |
| 2015-12-01 | 126 | 0.013328 |
| 2015-06-01 | 124 | 0.013117 |
| 2000-09-01 | 123 | 0.013011 |
| 1991-01-20 | 120 | 0.012694 |
| 1992-09-01 | 120 | 0.012694 |
| 2014-01-01 | 118 | 0.012482 |
| 2017-05-01 | 117 | 0.012376 |
| 1997-08-01 | 117 | 0.012376 |
| 1992-10-01 | 115 | 0.012165 |
| 2015-05-01 | 115 | 0.012165 |
| 2013-12-01 | 115 | 0.012165 |
| 2015-03-01 | 115 | 0.012165 |
| 2000-10-01 | 114 | 0.012059 |
| 2002-06-12 | 112 | 0.011847 |
| 1993-08-01 | 112 | 0.011847 |
| 2016-01-01 | 108 | 0.011424 |
| 2016-05-01 | 107 | 0.011319 |
| 2021-02-05 | 106 | 0.011213 |
| 1998-10-01 | 106 | 0.011213 |
| 1997-09-01 | 103 | 0.010895 |
| 2002-06-13 | 102 | 0.010790 |
| 2015-08-01 | 101 | 0.010684 |
| 2014-07-01 | 98 | 0.010367 |
| 2014-05-01 | 97 | 0.010261 |
| 2015-11-01 | 97 | 0.010261 |
| 2014-09-01 | 94 | 0.009943 |
| 2017-02-01 | 91 | 0.009626 |
| 2017-04-01 | 91 | 0.009626 |
| 2018-01-01 | 88 | 0.009309 |
| 2010-12-01 | 88 | 0.009309 |
| 2016-11-01 | 87 | 0.009203 |
| 2018-07-01 | 87 | 0.009203 |
| 2017-03-01 | 87 | 0.009203 |
| 2011-05-01 | 86 | 0.009097 |
| 2014-06-01 | 85 | 0.008991 |
| 2014-11-01 | 84 | 0.008886 |
| 2009-02-01 | 82 | 0.008674 |
| 2020-03-01 | 82 | 0.008674 |
| 2014-03-01 | 81 | 0.008568 |
| 2012-07-01 | 81 | 0.008568 |
| 2013-09-01 | 81 | 0.008568 |
| 2018-12-01 | 80 | 0.008462 |
| 2013-08-01 | 80 | 0.008462 |
| 1993-09-01 | 78 | 0.008251 |
| 2018-04-01 | 77 | 0.008145 |
| 2014-04-01 | 77 | 0.008145 |
| 2012-04-01 | 76 | 0.008039 |
| 2002-06-07 | 76 | 0.008039 |
| 2011-01-01 | 74 | 0.007828 |
| 2014-10-01 | 74 | 0.007828 |
| 2010-09-01 | 74 | 0.007828 |
| 1997-10-01 | 73 | 0.007722 |
| 2017-12-01 | 73 | 0.007722 |
| 2010-05-01 | 71 | 0.007510 |
| 2012-12-01 | 71 | 0.007510 |
| 1991-02-20 | 71 | 0.007510 |
| 2017-10-01 | 70 | 0.007405 |
| 2012-05-01 | 70 | 0.007405 |
| 2002-06-14 | 69 | 0.007299 |
| 2002-06-11 | 68 | 0.007193 |
| 2013-11-01 | 68 | 0.007193 |
| 2013-10-01 | 67 | 0.007087 |
| 2013-06-01 | 67 | 0.007087 |
| 2017-11-01 | 66 | 0.006982 |
| 2016-10-01 | 65 | 0.006876 |
| 2018-09-01 | 65 | 0.006876 |
| 2002-06-06 | 65 | 0.006876 |
| 2002-07-05 | 64 | 0.006770 |
| 2008-02-01 | 64 | 0.006770 |
| 2013-01-01 | 63 | 0.006664 |
| 2011-09-01 | 63 | 0.006664 |
| 2014-02-01 | 63 | 0.006664 |
| 2012-01-01 | 62 | 0.006558 |
| 2018-05-01 | 61 | 0.006453 |
| 2012-09-01 | 60 | 0.006347 |
| 2019-12-01 | 60 | 0.006347 |
| 2020-02-01 | 59 | 0.006241 |
| 2012-06-01 | 58 | 0.006135 |
| 2011-08-01 | 58 | 0.006135 |
| 2013-05-01 | 57 | 0.006030 |
| 2010-01-01 | 56 | 0.005924 |
| 2008-03-01 | 56 | 0.005924 |
| 2002-06-19 | 56 | 0.005924 |
| 1991-12-01 | 54 | 0.005712 |
| 2019-06-01 | 54 | 0.005712 |
| 2010-11-01 | 53 | 0.005606 |
| 2013-04-01 | 53 | 0.005606 |
| 2007-02-01 | 53 | 0.005606 |
| 2002-06-10 | 52 | 0.005501 |
| 2010-08-01 | 52 | 0.005501 |
| 1992-01-02 | 51 | 0.005395 |
| 2018-11-01 | 51 | 0.005395 |
| 2019-11-01 | 51 | 0.005395 |
| 2019-01-01 | 51 | 0.005395 |
| 2008-04-01 | 50 | 0.005289 |
| 2019-09-01 | 50 | 0.005289 |
| 2008-06-01 | 50 | 0.005289 |
| 2009-03-01 | 49 | 0.005183 |
| 2020-04-01 | 49 | 0.005183 |
| 2011-11-01 | 47 | 0.004972 |
| 1991-08-01 | 46 | 0.004866 |
| 2019-05-01 | 46 | 0.004866 |
| 2020-01-01 | 45 | 0.004760 |
| 1991-01-21 | 43 | 0.004549 |
| 1991-11-15 | 42 | 0.004443 |
| 2012-11-01 | 42 | 0.004443 |
| 1991-07-01 | 42 | 0.004443 |
| 2009-09-01 | 41 | 0.004337 |
| 2008-10-01 | 40 | 0.004231 |
| 2008-07-01 | 40 | 0.004231 |
| 2009-01-01 | 40 | 0.004231 |
| 1991-11-06 | 40 | 0.004231 |
| 2010-06-01 | 39 | 0.004125 |
| 2009-08-01 | 39 | 0.004125 |
| 1991-11-01 | 39 | 0.004125 |
| 2009-04-01 | 38 | 0.004020 |
| 2018-10-01 | 38 | 0.004020 |
| 2007-03-01 | 38 | 0.004020 |
| 2005-09-01 | 37 | 0.003914 |
| 1991-11-11 | 36 | 0.003808 |
| 2009-10-01 | 35 | 0.003702 |
| 2011-10-01 | 35 | 0.003702 |
| 2008-01-01 | 34 | 0.003597 |
| 2009-06-01 | 33 | 0.003491 |
| 2009-11-01 | 33 | 0.003491 |
| 2007-01-01 | 33 | 0.003491 |
| 2006-05-01 | 33 | 0.003491 |
| 2002-06-18 | 33 | 0.003491 |
| 2006-04-01 | 32 | 0.003385 |
| 1992-03-02 | 32 | 0.003385 |
| 2020-05-01 | 32 | 0.003385 |
| 2002-07-04 | 32 | 0.003385 |
| 2009-12-01 | 32 | 0.003385 |
| 2005-12-01 | 32 | 0.003385 |
| 2005-08-01 | 31 | 0.003279 |
| 2006-12-01 | 30 | 0.003173 |
| 2002-06-28 | 30 | 0.003173 |
| 2007-10-01 | 30 | 0.003173 |
| 2007-06-01 | 29 | 0.003068 |
| 2008-05-01 | 29 | 0.003068 |
| 2008-08-01 | 29 | 0.003068 |
| 2007-04-01 | 29 | 0.003068 |
| 2002-06-21 | 28 | 0.002962 |
| 1992-06-02 | 28 | 0.002962 |
| 2009-05-01 | 27 | 0.002856 |
| 1992-02-02 | 26 | 0.002750 |
| 2006-07-01 | 26 | 0.002750 |
| 1991-06-03 | 25 | 0.002645 |
| 1992-01-14 | 25 | 0.002645 |
| 1995-01-02 | 25 | 0.002645 |
| 2007-12-01 | 25 | 0.002645 |
| 1994-10-06 | 25 | 0.002645 |
| 2010-10-01 | 24 | 0.002539 |
| 1991-10-01 | 24 | 0.002539 |
| 2005-10-01 | 24 | 0.002539 |
| 1994-03-28 | 23 | 0.002433 |
| 2007-11-01 | 23 | 0.002433 |
| 2009-07-01 | 23 | 0.002433 |
| 2006-08-01 | 23 | 0.002433 |
| 2007-08-01 | 23 | 0.002433 |
| 1991-03-25 | 22 | 0.002327 |
| 2019-10-01 | 22 | 0.002327 |
| 1995-10-02 | 22 | 0.002327 |
| 2006-06-01 | 22 | 0.002327 |
| 2006-10-01 | 20 | 0.002116 |
| 2005-11-01 | 20 | 0.002116 |
| 2006-09-01 | 19 | 0.002010 |
| 2007-09-01 | 19 | 0.002010 |
| 2007-05-01 | 19 | 0.002010 |
| 1993-04-05 | 16 | 0.001692 |
| 2008-11-01 | 15 | 0.001587 |
| 1992-02-01 | 14 | 0.001481 |
| 1991-09-01 | 13 | 0.001375 |
| 1993-11-08 | 13 | 0.001375 |
| 1994-07-04 | 13 | 0.001375 |
| 2007-07-01 | 13 | 0.001375 |
| 1993-10-04 | 13 | 0.001375 |
| 1996-01-02 | 12 | 0.001269 |
| 1994-03-04 | 12 | 0.001269 |
| 1992-04-01 | 12 | 0.001269 |
| 2006-11-01 | 12 | 0.001269 |
| 1993-12-07 | 11 | 0.001164 |
| 1994-01-07 | 11 | 0.001164 |
| 2002-07-03 | 10 | 0.001058 |
| 2006-01-01 | 10 | 0.001058 |
| 1992-12-14 | 10 | 0.001058 |
| 1992-09-30 | 10 | 0.001058 |
| 1993-01-07 | 9 | 0.000952 |
| 1993-09-16 | 8 | 0.000846 |
| 1993-03-11 | 8 | 0.000846 |
| 1991-05-16 | 7 | 0.000740 |
| 2020-06-01 | 7 | 0.000740 |
| 2002-07-01 | 6 | 0.000635 |
| 2021-12-01 | 6 | 0.000635 |
| 1992-01-16 | 6 | 0.000635 |
| 1995-09-07 | 5 | 0.000529 |
| 1994-02-07 | 5 | 0.000529 |
| 1996-09-02 | 5 | 0.000529 |
| 1994-06-06 | 5 | 0.000529 |
| 2021-06-01 | 5 | 0.000529 |
| 1993-02-12 | 5 | 0.000529 |
| 2002-06-05 | 5 | 0.000529 |
| 1993-08-05 | 5 | 0.000529 |
| 1991-01-10 | 4 | 0.000423 |
| 1990-12-10 | 4 | 0.000423 |
| 2020-08-01 | 4 | 0.000423 |
| 1995-05-02 | 4 | 0.000423 |
| 2021-07-01 | 4 | 0.000423 |
| 2021-11-02 | 4 | 0.000423 |
| 2021-04-01 | 3 | 0.000317 |
| 2002-06-27 | 3 | 0.000317 |
| 2002-06-25 | 3 | 0.000317 |
| 2002-06-26 | 3 | 0.000317 |
| 1991-04-02 | 3 | 0.000317 |
| 1990-10-01 | 3 | 0.000317 |
| 2022-11-02 | 3 | 0.000317 |
| 1991-01-01 | 3 | 0.000317 |
| 2020-10-05 | 3 | 0.000317 |
| 1991-05-08 | 3 | 0.000317 |
| 1992-09-25 | 3 | 0.000317 |
| 2002-07-02 | 3 | 0.000317 |
| 1992-10-14 | 3 | 0.000317 |
| 1996-08-02 | 3 | 0.000317 |
| 1993-04-12 | 3 | 0.000317 |
| 1996-09-30 | 2 | 0.000212 |
| 1993-02-17 | 2 | 0.000212 |
| 1994-12-04 | 2 | 0.000212 |
| 1995-10-21 | 2 | 0.000212 |
| 1997-05-04 | 2 | 0.000212 |
| 2002-05-28 | 2 | 0.000212 |
| 1999-12-28 | 2 | 0.000212 |
| 1998-08-18 | 2 | 0.000212 |
| 2002-06-01 | 2 | 0.000212 |
| 2021-08-02 | 2 | 0.000212 |
| 1990-03-01 | 2 | 0.000212 |
| 1990-02-20 | 2 | 0.000212 |
| 1990-09-25 | 2 | 0.000212 |
| 1998-08-21 | 2 | 0.000212 |
| 1993-02-09 | 2 | 0.000212 |
| 1994-08-19 | 2 | 0.000212 |
| 1998-12-18 | 2 | 0.000212 |
| 1991-10-25 | 2 | 0.000212 |
| 1996-12-21 | 2 | 0.000212 |
| 2022-01-03 | 2 | 0.000212 |
| 2002-06-03 | 2 | 0.000212 |
| 1993-07-05 | 2 | 0.000212 |
| 2002-07-19 | 1 | 0.000106 |
| 2002-07-12 | 1 | 0.000106 |
| 2002-07-28 | 1 | 0.000106 |
| 2002-06-04 | 1 | 0.000106 |
| 2002-07-10 | 1 | 0.000106 |
| 1992-09-23 | 1 | 0.000106 |
| 1992-05-18 | 1 | 0.000106 |
| 2023-06-01 | 1 | 0.000106 |
| 1996-09-05 | 1 | 0.000106 |
| 1991-03-15 | 1 | 0.000106 |
| 1996-06-03 | 1 | 0.000106 |
| 2021-02-01 | 1 | 0.000106 |
| 1991-10-21 | 1 | 0.000106 |
| 1990-01-01 | 1 | 0.000106 |
| 1994-10-10 | 1 | 0.000106 |
| 1992-09-19 | 1 | 0.000106 |
| 1996-07-12 | 1 | 0.000106 |
| 1994-07-28 | 1 | 0.000106 |
| 1999-04-22 | 1 | 0.000106 |
| 2021-05-03 | 1 | 0.000106 |
| 1996-03-20 | 1 | 0.000106 |
| 1996-12-26 | 1 | 0.000106 |
| 2000-02-14 | 1 | 0.000106 |
| 2000-03-30 | 1 | 0.000106 |
| 1991-06-01 | 1 | 0.000106 |
| 2014-04-05 | 1 | 0.000106 |
| 2000-04-26 | 1 | 0.000106 |
| 1990-01-10 | 1 | 0.000106 |
| 1991-01-08 | 1 | 0.000106 |
| 1994-01-10 | 1 | 0.000106 |
| 1991-04-01 | 1 | 0.000106 |
| 1996-12-28 | 1 | 0.000106 |
| 1997-06-04 | 1 | 0.000106 |
| 1992-08-14 | 1 | 0.000106 |
| 1992-05-15 | 1 | 0.000106 |
| 1990-04-20 | 1 | 0.000106 |
| 1994-02-14 | 1 | 0.000106 |
| 1991-09-03 | 1 | 0.000106 |
| 1991-01-31 | 1 | 0.000106 |
| 1992-03-24 | 1 | 0.000106 |
| 1995-07-19 | 1 | 0.000106 |
| 1992-06-10 | 1 | 0.000106 |
| 1992-04-02 | 1 | 0.000106 |
| 1990-11-01 | 1 | 0.000106 |
| 1996-09-25 | 1 | 0.000106 |
| 2002-07-17 | 1 | 0.000106 |
| 2002-07-27 | 1 | 0.000106 |
| 1993-04-03 | 1 | 0.000106 |
| 1994-10-11 | 1 | 0.000106 |
| 1990-01-20 | 1 | 0.000106 |
| 1993-02-15 | 1 | 0.000106 |
| 1999-03-25 | 1 | 0.000106 |
| 1993-05-04 | 1 | 0.000106 |
| 1993-02-05 | 1 | 0.000106 |
| 1994-09-10 | 1 | 0.000106 |
| 1993-02-04 | 1 | 0.000106 |
| 1993-02-08 | 1 | 0.000106 |
| 1997-04-02 | 1 | 0.000106 |
| 1999-08-12 | 1 | 0.000106 |
| 2000-03-28 | 1 | 0.000106 |
| 1990-06-10 | 1 | 0.000106 |
| 1991-04-23 | 1 | 0.000106 |
| 1998-10-20 | 1 | 0.000106 |
| 1992-03-01 | 1 | 0.000106 |
| 1993-04-19 | 1 | 0.000106 |
| 1993-03-10 | 1 | 0.000106 |
| 1991-05-05 | 1 | 0.000106 |
| 1998-02-05 | 1 | 0.000106 |
| 1999-10-25 | 1 | 0.000106 |
| 1991-04-04 | 1 | 0.000106 |
| 1998-02-09 | 1 | 0.000106 |
| 2022-06-01 | 1 | 0.000106 |
| 1993-04-30 | 1 | 0.000106 |
| 1999-10-27 | 1 | 0.000106 |
| 1995-10-11 | 1 | 0.000106 |
| 1990-03-31 | 1 | 0.000106 |
| 1992-05-21 | 1 | 0.000106 |
| 1995-10-26 | 1 | 0.000106 |
| 1996-11-09 | 1 | 0.000106 |
| 1992-09-29 | 1 | 0.000106 |
| 1991-08-03 | 1 | 0.000106 |
| 1992-11-02 | 1 | 0.000106 |
| 1991-03-20 | 1 | 0.000106 |
| 2022-03-09 | 1 | 0.000106 |
| 2022-05-02 | 1 | 0.000106 |
| 2023-04-03 | 1 | 0.000106 |
| 2022-07-04 | 1 | 0.000106 |
| 1992-05-22 | 1 | 0.000106 |
| 1993-03-08 | 1 | 0.000106 |
| 1991-06-20 | 1 | 0.000106 |
| 2020-12-01 | 1 | 0.000106 |
| 1991-01-04 | 1 | 0.000106 |
| 1990-02-12 | 1 | 0.000106 |
| 1991-12-03 | 1 | 0.000106 |
| 1996-12-20 | 1 | 0.000106 |
| 1992-06-08 | 1 | 0.000106 |
| 1996-12-13 | 1 | 0.000106 |
| 1992-11-19 | 1 | 0.000106 |
| 1993-03-16 | 1 | 0.000106 |
| 1992-02-03 | 1 | 0.000106 |
| 1996-01-30 | 1 | 0.000106 |
# Vamos a realizar analisis por cada variable
var = "msf_datelastrecurringdonorquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastrecurringdonorquota__c es 48715. Lo que supone un 4.9005899016562315% El nº de vacios para la variable msf_datelastrecurringdonorquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2023-07-04 | 393458 | 41.620396 |
| 2023-06-02 | 22387 | 2.368120 |
| 2023-05-03 | 21046 | 2.226268 |
| 2023-01-03 | 14510 | 1.534883 |
| 2023-02-02 | 13748 | 1.454278 |
| 2023-03-02 | 11212 | 1.186017 |
| 2022-12-02 | 10506 | 1.111336 |
| 2023-04-04 | 9691 | 1.025124 |
| 2022-11-03 | 8475 | 0.896494 |
| 2022-10-04 | 7566 | 0.800339 |
| 2022-08-02 | 7167 | 0.758133 |
| 2020-09-01 | 6699 | 0.708627 |
| 2022-09-02 | 6368 | 0.673614 |
| 2018-02-01 | 5264 | 0.556831 |
| 2018-01-03 | 4175 | 0.441636 |
| 2021-04-02 | 3879 | 0.410325 |
| 2022-01-04 | 3867 | 0.409055 |
| 2017-12-04 | 3853 | 0.407574 |
| 2019-01-02 | 3847 | 0.406940 |
| 2018-12-03 | 3757 | 0.397419 |
| 2021-01-05 | 3666 | 0.387793 |
| 2019-12-02 | 3651 | 0.386207 |
| 2021-12-02 | 3630 | 0.383985 |
| 2021-07-02 | 3591 | 0.379860 |
| 2021-10-02 | 3581 | 0.378802 |
| 2018-03-01 | 3569 | 0.377533 |
| 2021-11-03 | 3438 | 0.363675 |
| 2021-06-02 | 3421 | 0.361877 |
| 2022-07-05 | 3410 | 0.360713 |
| 2022-05-03 | 3400 | 0.359656 |
| 2021-03-02 | 3396 | 0.359232 |
| 2021-05-04 | 3395 | 0.359127 |
| 2018-09-03 | 3323 | 0.351510 |
| 2022-02-02 | 3290 | 0.348020 |
| 2021-02-02 | 3277 | 0.346644 |
| 2018-10-02 | 3275 | 0.346433 |
| 2020-01-02 | 3258 | 0.344635 |
| 2022-04-02 | 3253 | 0.344106 |
| 2018-04-03 | 3230 | 0.341673 |
| 2018-07-02 | 3223 | 0.340932 |
| 2022-06-02 | 3201 | 0.338605 |
| 2019-02-01 | 3144 | 0.332576 |
| 2021-08-03 | 3143 | 0.332470 |
| 2020-02-03 | 3126 | 0.330672 |
| 2017-10-02 | 3114 | 0.329402 |
| 2019-10-02 | 3114 | 0.329402 |
| 2018-08-01 | 3107 | 0.328662 |
| 2020-03-02 | 3099 | 0.327815 |
| 2019-08-01 | 3098 | 0.327710 |
| 2022-03-02 | 3095 | 0.327392 |
| 2018-06-01 | 3075 | 0.325277 |
| 2019-09-02 | 3063 | 0.324007 |
| 2017-11-02 | 3032 | 0.320728 |
| 2018-11-02 | 3027 | 0.320199 |
| 2019-07-01 | 3024 | 0.319882 |
| 2019-03-01 | 2966 | 0.313747 |
| 2017-01-02 | 2947 | 0.311737 |
| 2016-12-01 | 2909 | 0.307717 |
| 2018-05-03 | 2882 | 0.304861 |
| 2017-09-01 | 2868 | 0.303380 |
| 2019-04-01 | 2811 | 0.297351 |
| 2019-11-04 | 2788 | 0.294918 |
| 2021-09-02 | 2681 | 0.283599 |
| 2019-05-02 | 2640 | 0.279262 |
| 2017-08-01 | 2596 | 0.274608 |
| 2019-06-03 | 2538 | 0.268472 |
| 2017-07-03 | 2517 | 0.266251 |
| 2015-12-02 | 2422 | 0.256202 |
| 2016-10-03 | 2399 | 0.253769 |
| 2017-02-02 | 2394 | 0.253240 |
| 2017-03-02 | 2383 | 0.252076 |
| 2017-05-02 | 2360 | 0.249643 |
| 2020-04-02 | 2339 | 0.247422 |
| 2017-06-01 | 2336 | 0.247105 |
| 2016-02-01 | 2269 | 0.240017 |
| 2017-04-03 | 2269 | 0.240017 |
| 2016-11-02 | 2265 | 0.239594 |
| 2012-12-03 | 2252 | 0.238219 |
| 2016-01-04 | 2217 | 0.234517 |
| 2016-09-01 | 2209 | 0.233670 |
| 2020-07-01 | 2190 | 0.231660 |
| 2015-01-02 | 2138 | 0.226160 |
| 2012-01-02 | 2137 | 0.226054 |
| 2014-01-02 | 2131 | 0.225419 |
| 2014-12-02 | 2112 | 0.223410 |
| 2020-05-03 | 2102 | 0.222352 |
| 2016-08-01 | 2088 | 0.220871 |
| 2015-10-01 | 2083 | 0.220342 |
| 2016-07-01 | 2081 | 0.220130 |
| 2020-06-02 | 2066 | 0.218544 |
| 2013-01-02 | 2053 | 0.217168 |
| 2016-03-01 | 2029 | 0.214630 |
| 2012-10-01 | 2027 | 0.214418 |
| 2016-04-01 | 2026 | 0.214312 |
| 2012-02-01 | 2020 | 0.213678 |
| 2015-09-01 | 2014 | 0.213043 |
| 2011-12-01 | 2009 | 0.212514 |
| 2016-06-01 | 2003 | 0.211879 |
| 2013-02-01 | 1981 | 0.209552 |
| 2015-02-02 | 1980 | 0.209446 |
| 2015-03-02 | 1975 | 0.208918 |
| 2015-11-03 | 1971 | 0.208494 |
| 2015-04-01 | 1963 | 0.207648 |
| 2014-02-03 | 1962 | 0.207542 |
| 2016-05-02 | 1924 | 0.203523 |
| 2013-12-02 | 1909 | 0.201936 |
| 2015-07-01 | 1898 | 0.200772 |
| 2015-06-02 | 1880 | 0.198868 |
| 2013-03-01 | 1831 | 0.193685 |
| 2015-08-03 | 1819 | 0.192416 |
| 2012-03-01 | 1807 | 0.191146 |
| 2012-04-02 | 1805 | 0.190935 |
| 2013-10-02 | 1787 | 0.189031 |
| 2012-05-03 | 1787 | 0.189031 |
| 2012-11-02 | 1780 | 0.188290 |
| 2014-03-03 | 1775 | 0.187761 |
| 2020-11-04 | 1766 | 0.186809 |
| 2014-10-02 | 1761 | 0.186280 |
| 2012-09-03 | 1761 | 0.186280 |
| 2012-07-02 | 1753 | 0.185434 |
| 2015-05-04 | 1730 | 0.183001 |
| 2013-04-02 | 1709 | 0.180780 |
| 2013-09-02 | 1666 | 0.176231 |
| 2014-09-03 | 1655 | 0.175068 |
| 2014-04-02 | 1632 | 0.172635 |
| 2012-08-01 | 1632 | 0.172635 |
| 2014-05-05 | 1627 | 0.172106 |
| 2014-08-01 | 1623 | 0.171683 |
| 2014-07-02 | 1601 | 0.169355 |
| 2014-11-03 | 1599 | 0.169144 |
| 2020-10-02 | 1593 | 0.168509 |
| 2013-07-01 | 1581 | 0.167240 |
| 2010-12-02 | 1537 | 0.162585 |
| 2011-01-03 | 1536 | 0.162480 |
| 2011-03-01 | 1530 | 0.161845 |
| 2013-05-02 | 1519 | 0.160681 |
| 2012-06-04 | 1518 | 0.160576 |
| 2011-11-02 | 1480 | 0.156556 |
| 2014-06-05 | 1471 | 0.155604 |
| 2020-12-02 | 1437 | 0.152007 |
| 2011-10-04 | 1432 | 0.151478 |
| 2011-09-02 | 1428 | 0.151055 |
| 2011-02-01 | 1422 | 0.150421 |
| 2013-11-04 | 1418 | 0.149998 |
| 2011-07-01 | 1386 | 0.146613 |
| 2011-06-01 | 1368 | 0.144708 |
| 2013-08-02 | 1362 | 0.144074 |
| 2011-04-01 | 1350 | 0.142804 |
| 2013-06-03 | 1342 | 0.141958 |
| 2011-05-02 | 1273 | 0.134659 |
| 2009-02-03 | 1268 | 0.134130 |
| 2009-01-02 | 1245 | 0.131697 |
| 2011-08-02 | 1238 | 0.130957 |
| 2010-10-04 | 1215 | 0.128524 |
| 2008-12-01 | 1205 | 0.127466 |
| 2008-10-02 | 1187 | 0.125562 |
| 2010-09-02 | 1180 | 0.124822 |
| 2009-12-02 | 1179 | 0.124716 |
| 2010-11-02 | 1173 | 0.124081 |
| 2008-09-01 | 1155 | 0.122177 |
| 2010-08-02 | 1097 | 0.116042 |
| 2010-07-01 | 1080 | 0.114244 |
| 2008-01-03 | 1066 | 0.112763 |
| 2010-05-03 | 1066 | 0.112763 |
| 2008-03-03 | 1042 | 0.110224 |
| 2010-06-02 | 1041 | 0.110118 |
| 2020-08-03 | 1034 | 0.109378 |
| 2010-04-01 | 1034 | 0.109378 |
| 2009-03-03 | 1031 | 0.109060 |
| 2010-02-01 | 1026 | 0.108531 |
| 2008-02-04 | 1026 | 0.108531 |
| 2010-03-01 | 1024 | 0.108320 |
| 2009-04-02 | 1023 | 0.108214 |
| 2010-01-04 | 1022 | 0.108108 |
| 2009-10-02 | 1021 | 0.108002 |
| 2007-12-02 | 982 | 0.103877 |
| 2009-11-02 | 967 | 0.102290 |
| 2008-11-03 | 945 | 0.099963 |
| 2008-07-04 | 945 | 0.099963 |
| 2009-09-02 | 938 | 0.099223 |
| 2008-05-02 | 926 | 0.097953 |
| 2009-05-04 | 895 | 0.094674 |
| 2008-04-04 | 894 | 0.094568 |
| 2008-06-02 | 866 | 0.091606 |
| 2007-04-02 | 863 | 0.091289 |
| 2009-06-04 | 846 | 0.089491 |
| 2009-07-02 | 842 | 0.089068 |
| 2007-10-04 | 840 | 0.088856 |
| 2007-11-02 | 836 | 0.088433 |
| 2007-07-04 | 806 | 0.085260 |
| 2007-09-03 | 804 | 0.085048 |
| 2007-01-04 | 772 | 0.081663 |
| 2007-03-02 | 768 | 0.081240 |
| 2009-08-03 | 760 | 0.080394 |
| 2008-08-08 | 749 | 0.079230 |
| 2007-02-05 | 716 | 0.075739 |
| 2007-05-04 | 702 | 0.074258 |
| 2007-08-02 | 698 | 0.073835 |
| 2007-06-05 | 646 | 0.068335 |
| 2002-05-01 | 643 | 0.068017 |
| 2006-10-02 | 627 | 0.066325 |
| 2006-02-03 | 601 | 0.063574 |
| 2006-01-05 | 597 | 0.063151 |
| 2006-12-02 | 571 | 0.060401 |
| 2006-09-04 | 541 | 0.057228 |
| 2005-12-03 | 532 | 0.056276 |
| 2006-06-02 | 521 | 0.055112 |
| 2006-03-03 | 521 | 0.055112 |
| 2006-04-03 | 520 | 0.055006 |
| 2006-07-03 | 518 | 0.054795 |
| 2006-11-03 | 518 | 0.054795 |
| 2006-05-04 | 507 | 0.053631 |
| 2006-08-02 | 494 | 0.052256 |
| 2005-10-03 | 448 | 0.047390 |
| 2005-04-04 | 441 | 0.046649 |
| 2005-09-02 | 439 | 0.046438 |
| 2005-03-04 | 428 | 0.045274 |
| 2005-08-02 | 423 | 0.044745 |
| 2005-02-04 | 418 | 0.044216 |
| 2005-11-03 | 406 | 0.042947 |
| 2005-05-04 | 396 | 0.041889 |
| 2005-01-04 | 390 | 0.041255 |
| 2005-06-03 | 385 | 0.040726 |
| 2004-09-03 | 380 | 0.040197 |
| 2005-07-04 | 357 | 0.037764 |
| 2004-02-01 | 355 | 0.037552 |
| 2004-03-01 | 328 | 0.034696 |
| 2004-01-01 | 322 | 0.034061 |
| 2004-07-01 | 321 | 0.033956 |
| 2004-10-06 | 315 | 0.033321 |
| 2004-04-01 | 307 | 0.032475 |
| 2003-01-01 | 286 | 0.030253 |
| 2004-11-04 | 286 | 0.030253 |
| 2003-02-01 | 277 | 0.029301 |
| 2004-12-05 | 273 | 0.028878 |
| 2000-10-01 | 267 | 0.028244 |
| 2004-05-01 | 263 | 0.027820 |
| 2002-10-01 | 254 | 0.026868 |
| 2001-10-01 | 250 | 0.026445 |
| 2001-02-01 | 242 | 0.025599 |
| 2003-04-01 | 236 | 0.024964 |
| 2004-06-01 | 236 | 0.024964 |
| 2003-12-01 | 235 | 0.024859 |
| 2002-01-01 | 233 | 0.024647 |
| 2003-10-01 | 215 | 0.022743 |
| 2002-09-01 | 207 | 0.021897 |
| 2003-07-01 | 206 | 0.021791 |
| 2003-09-01 | 206 | 0.021791 |
| 2004-08-01 | 205 | 0.021685 |
| 2003-03-01 | 203 | 0.021474 |
| 2001-07-01 | 200 | 0.021156 |
| 2003-11-01 | 199 | 0.021050 |
| 1996-10-01 | 194 | 0.020522 |
| 2001-04-01 | 187 | 0.019781 |
| 2003-06-01 | 187 | 0.019781 |
| 2002-08-01 | 186 | 0.019675 |
| 2001-01-01 | 185 | 0.019569 |
| 2003-05-01 | 182 | 0.019252 |
| 2001-03-01 | 178 | 0.018829 |
| 2003-08-01 | 176 | 0.018617 |
| 2001-12-01 | 171 | 0.018089 |
| 2002-12-01 | 170 | 0.017983 |
| 2002-11-01 | 168 | 0.017771 |
| 2002-02-01 | 165 | 0.017454 |
| 1997-10-01 | 162 | 0.017137 |
| 1997-01-01 | 162 | 0.017137 |
| 2000-12-01 | 157 | 0.016608 |
| 2000-04-05 | 155 | 0.016396 |
| 2000-07-01 | 154 | 0.016290 |
| 1998-02-01 | 154 | 0.016290 |
| 2002-04-01 | 153 | 0.016184 |
| 2001-09-01 | 152 | 0.016079 |
| 2001-11-01 | 151 | 0.015973 |
| 1997-02-01 | 148 | 0.015656 |
| 1995-10-02 | 146 | 0.015444 |
| 1999-02-01 | 146 | 0.015444 |
| 2000-02-01 | 145 | 0.015338 |
| 2002-03-01 | 145 | 0.015338 |
| 1997-04-01 | 143 | 0.015127 |
| 1996-04-01 | 143 | 0.015127 |
| 1999-01-01 | 136 | 0.014386 |
| 1996-07-01 | 133 | 0.014069 |
| 2000-09-01 | 131 | 0.013857 |
| 1996-02-01 | 130 | 0.013752 |
| 2001-06-01 | 130 | 0.013752 |
| 1999-07-01 | 129 | 0.013646 |
| 1997-07-01 | 129 | 0.013646 |
| 2000-05-01 | 124 | 0.013117 |
| 1999-04-01 | 123 | 0.013011 |
| 1998-01-01 | 122 | 0.012905 |
| 2001-08-01 | 121 | 0.012800 |
| 1998-10-01 | 120 | 0.012694 |
| 1998-04-01 | 120 | 0.012694 |
| 2000-08-01 | 118 | 0.012482 |
| 2001-05-01 | 116 | 0.012271 |
| 2000-11-01 | 114 | 0.012059 |
| 2000-06-01 | 109 | 0.011530 |
| 2017-07-01 | 108 | 0.011424 |
| 1998-03-01 | 108 | 0.011424 |
| 1996-01-02 | 107 | 0.011319 |
| 2000-01-13 | 107 | 0.011319 |
| 1995-02-01 | 107 | 0.011319 |
| 1999-10-01 | 107 | 0.011319 |
| 2002-07-10 | 104 | 0.011001 |
| 2000-03-09 | 100 | 0.010578 |
| 1999-12-01 | 99 | 0.010472 |
| 1998-07-01 | 97 | 0.010261 |
| 1995-07-01 | 97 | 0.010261 |
| 1995-04-01 | 91 | 0.009626 |
| 1999-03-01 | 90 | 0.009520 |
| 1998-12-01 | 89 | 0.009415 |
| 1997-03-01 | 85 | 0.008991 |
| 2002-07-13 | 83 | 0.008780 |
| 1995-01-02 | 83 | 0.008780 |
| 1999-06-01 | 82 | 0.008674 |
| 1999-09-01 | 75 | 0.007934 |
| 2017-01-01 | 73 | 0.007722 |
| 1996-03-01 | 73 | 0.007722 |
| 2017-12-01 | 71 | 0.007510 |
| 1999-08-01 | 70 | 0.007405 |
| 1996-12-01 | 68 | 0.007193 |
| 1999-11-01 | 68 | 0.007193 |
| 2015-12-01 | 67 | 0.007087 |
| 2002-06-13 | 66 | 0.006982 |
| 2017-02-01 | 66 | 0.006982 |
| 2018-01-01 | 66 | 0.006982 |
| 2015-02-01 | 65 | 0.006876 |
| 2017-05-01 | 64 | 0.006770 |
| 2017-03-01 | 63 | 0.006664 |
| 2015-05-01 | 63 | 0.006664 |
| 2017-11-01 | 61 | 0.006453 |
| 2016-05-01 | 61 | 0.006453 |
| 2015-08-01 | 61 | 0.006453 |
| 1995-11-01 | 59 | 0.006241 |
| 1997-06-01 | 59 | 0.006241 |
| 2015-06-01 | 59 | 0.006241 |
| 1998-09-01 | 58 | 0.006135 |
| 1995-03-01 | 57 | 0.006030 |
| 2016-10-01 | 57 | 0.006030 |
| 2015-11-01 | 56 | 0.005924 |
| 1998-06-01 | 55 | 0.005818 |
| 2015-03-01 | 55 | 0.005818 |
| 1996-09-02 | 55 | 0.005818 |
| 1996-11-01 | 54 | 0.005712 |
| 2016-01-01 | 53 | 0.005606 |
| 2017-04-01 | 52 | 0.005501 |
| 2015-01-01 | 52 | 0.005501 |
| 1994-10-06 | 52 | 0.005501 |
| 2014-03-01 | 52 | 0.005501 |
| 1995-06-01 | 52 | 0.005501 |
| 1999-05-01 | 52 | 0.005501 |
| 2018-10-01 | 51 | 0.005395 |
| 2014-12-01 | 50 | 0.005289 |
| 2017-10-01 | 50 | 0.005289 |
| 2016-11-01 | 50 | 0.005289 |
| 1998-08-01 | 50 | 0.005289 |
| 2014-09-01 | 50 | 0.005289 |
| 2013-12-01 | 49 | 0.005183 |
| 2018-04-01 | 48 | 0.005077 |
| 2013-09-01 | 48 | 0.005077 |
| 2018-07-01 | 47 | 0.004972 |
| 1998-11-01 | 47 | 0.004972 |
| 1995-12-01 | 47 | 0.004972 |
| 2014-05-01 | 46 | 0.004866 |
| 1997-09-01 | 46 | 0.004866 |
| 2012-05-01 | 45 | 0.004760 |
| 1994-10-01 | 45 | 0.004760 |
| 1997-11-01 | 45 | 0.004760 |
| 2009-02-01 | 45 | 0.004760 |
| 2012-04-01 | 44 | 0.004654 |
| 2018-12-01 | 44 | 0.004654 |
| 1997-08-01 | 43 | 0.004549 |
| 2019-12-01 | 42 | 0.004443 |
| 1996-06-03 | 42 | 0.004443 |
| 2018-11-01 | 41 | 0.004337 |
| 1997-05-01 | 40 | 0.004231 |
| 1997-12-01 | 40 | 0.004231 |
| 1994-07-04 | 39 | 0.004125 |
| 2013-11-01 | 39 | 0.004125 |
| 1996-05-01 | 38 | 0.004020 |
| 2012-07-01 | 38 | 0.004020 |
| 1995-09-07 | 38 | 0.004020 |
| 1998-05-01 | 37 | 0.003914 |
| 2020-02-01 | 37 | 0.003914 |
| 1994-11-01 | 37 | 0.003914 |
| 2014-10-01 | 37 | 0.003914 |
| 1994-02-01 | 36 | 0.003808 |
| 2014-07-01 | 35 | 0.003702 |
| 2012-09-01 | 35 | 0.003702 |
| 2014-11-01 | 35 | 0.003702 |
| 2008-10-01 | 35 | 0.003702 |
| 1996-08-02 | 34 | 0.003597 |
| 1994-12-04 | 34 | 0.003597 |
| 2014-04-01 | 34 | 0.003597 |
| 2013-10-01 | 33 | 0.003491 |
| 2014-06-01 | 33 | 0.003491 |
| 2011-05-01 | 33 | 0.003491 |
| 1994-03-28 | 32 | 0.003385 |
| 2010-12-01 | 31 | 0.003279 |
| 2020-03-01 | 31 | 0.003279 |
| 2019-05-01 | 31 | 0.003279 |
| 2020-01-01 | 31 | 0.003279 |
| 2009-03-01 | 30 | 0.003173 |
| 2019-09-01 | 30 | 0.003173 |
| 1994-01-07 | 30 | 0.003173 |
| 2013-05-01 | 30 | 0.003173 |
| 2014-02-01 | 30 | 0.003173 |
| 2018-05-01 | 29 | 0.003068 |
| 2002-06-05 | 29 | 0.003068 |
| 2013-01-01 | 28 | 0.002962 |
| 2018-09-01 | 28 | 0.002962 |
| 2019-11-01 | 28 | 0.002962 |
| 2012-01-01 | 28 | 0.002962 |
| 1995-05-02 | 27 | 0.002856 |
| 2009-04-01 | 27 | 0.002856 |
| 2019-01-01 | 27 | 0.002856 |
| 2011-01-01 | 27 | 0.002856 |
| 2020-04-01 | 27 | 0.002856 |
| 2012-11-01 | 27 | 0.002856 |
| 1993-07-01 | 27 | 0.002856 |
| 1993-04-05 | 26 | 0.002750 |
| 2009-09-01 | 26 | 0.002750 |
| 2002-06-08 | 26 | 0.002750 |
| 2009-08-01 | 26 | 0.002750 |
| 2009-01-01 | 26 | 0.002750 |
| 2011-11-01 | 25 | 0.002645 |
| 2012-12-01 | 25 | 0.002645 |
| 2010-05-01 | 25 | 0.002645 |
| 2011-10-01 | 25 | 0.002645 |
| 2011-08-01 | 25 | 0.002645 |
| 1993-10-04 | 24 | 0.002539 |
| 2013-04-01 | 24 | 0.002539 |
| 2008-06-01 | 24 | 0.002539 |
| 2008-04-01 | 24 | 0.002539 |
| 2019-10-01 | 23 | 0.002433 |
| 2013-06-01 | 23 | 0.002433 |
| 2008-02-01 | 23 | 0.002433 |
| 2020-06-01 | 22 | 0.002327 |
| 2020-05-01 | 22 | 0.002327 |
| 1993-07-05 | 22 | 0.002327 |
| 1995-08-01 | 22 | 0.002327 |
| 2010-11-01 | 21 | 0.002221 |
| 2019-06-01 | 20 | 0.002116 |
| 2008-07-01 | 20 | 0.002116 |
| 2009-10-01 | 20 | 0.002116 |
| 2014-01-01 | 20 | 0.002116 |
| 2011-09-01 | 20 | 0.002116 |
| 2008-11-01 | 20 | 0.002116 |
| 2012-06-01 | 20 | 0.002116 |
| 2008-03-01 | 19 | 0.002010 |
| 2007-11-01 | 19 | 0.002010 |
| 2013-08-01 | 19 | 0.002010 |
| 2005-12-01 | 18 | 0.001904 |
| 2010-06-01 | 18 | 0.001904 |
| 1992-06-01 | 18 | 0.001904 |
| 2009-06-01 | 18 | 0.001904 |
| 1995-10-01 | 18 | 0.001904 |
| 2010-01-01 | 17 | 0.001798 |
| 2010-09-01 | 17 | 0.001798 |
| 2021-02-05 | 17 | 0.001798 |
| 2009-05-01 | 17 | 0.001798 |
| 2007-08-01 | 17 | 0.001798 |
| 1994-02-07 | 17 | 0.001798 |
| 2008-08-01 | 17 | 0.001798 |
| 2010-08-01 | 17 | 0.001798 |
| 1992-11-01 | 17 | 0.001798 |
| 2006-04-01 | 16 | 0.001692 |
| 1993-03-01 | 16 | 0.001692 |
| 2009-12-01 | 16 | 0.001692 |
| 1993-11-08 | 16 | 0.001692 |
| 1994-09-01 | 16 | 0.001692 |
| 1994-07-01 | 15 | 0.001587 |
| 1994-03-04 | 15 | 0.001587 |
| 2008-05-01 | 15 | 0.001587 |
| 2006-11-01 | 15 | 0.001587 |
| 2006-07-01 | 15 | 0.001587 |
| 1993-01-07 | 14 | 0.001481 |
| 1992-12-01 | 14 | 0.001481 |
| 1993-01-01 | 14 | 0.001481 |
| 1995-01-01 | 14 | 0.001481 |
| 2000-01-01 | 14 | 0.001481 |
| 2007-10-01 | 13 | 0.001375 |
| 2007-03-01 | 13 | 0.001375 |
| 2005-08-01 | 13 | 0.001375 |
| 2008-01-01 | 13 | 0.001375 |
| 1993-05-01 | 13 | 0.001375 |
| 1996-06-01 | 13 | 0.001375 |
| 1994-03-01 | 13 | 0.001375 |
| 2009-07-01 | 13 | 0.001375 |
| 2006-10-01 | 12 | 0.001269 |
| 1996-01-01 | 12 | 0.001269 |
| 2006-05-01 | 12 | 0.001269 |
| 2000-03-01 | 12 | 0.001269 |
| 2007-04-01 | 11 | 0.001164 |
| 2006-09-01 | 11 | 0.001164 |
| 2010-10-01 | 11 | 0.001164 |
| 2006-06-01 | 11 | 0.001164 |
| 1993-08-05 | 10 | 0.001058 |
| 2007-09-01 | 10 | 0.001058 |
| 2007-06-01 | 10 | 0.001058 |
| 2007-01-01 | 10 | 0.001058 |
| 2009-11-01 | 10 | 0.001058 |
| 2007-12-01 | 10 | 0.001058 |
| 2005-09-01 | 9 | 0.000952 |
| 1993-11-01 | 9 | 0.000952 |
| 1994-01-01 | 9 | 0.000952 |
| 2005-11-01 | 9 | 0.000952 |
| 2023-06-01 | 9 | 0.000952 |
| 1994-06-06 | 9 | 0.000952 |
| 2007-02-01 | 8 | 0.000846 |
| 1992-07-01 | 8 | 0.000846 |
| 2022-12-01 | 8 | 0.000846 |
| 1993-12-07 | 8 | 0.000846 |
| 2020-08-01 | 8 | 0.000846 |
| 1994-08-01 | 8 | 0.000846 |
| 2023-07-03 | 8 | 0.000846 |
| 1993-02-17 | 8 | 0.000846 |
| 1992-04-02 | 7 | 0.000740 |
| 1994-05-09 | 7 | 0.000740 |
| 1993-09-16 | 7 | 0.000740 |
| 2007-07-01 | 7 | 0.000740 |
| 1992-10-14 | 7 | 0.000740 |
| 2007-05-01 | 7 | 0.000740 |
| 1992-12-14 | 6 | 0.000635 |
| 1993-12-01 | 6 | 0.000635 |
| 2006-12-01 | 6 | 0.000635 |
| 1992-01-02 | 6 | 0.000635 |
| 2006-08-01 | 6 | 0.000635 |
| 2005-10-01 | 6 | 0.000635 |
| 2022-11-02 | 6 | 0.000635 |
| 1995-05-01 | 5 | 0.000529 |
| 1991-01-20 | 5 | 0.000529 |
| 1996-08-01 | 5 | 0.000529 |
| 1995-09-01 | 5 | 0.000529 |
| 1994-06-01 | 5 | 0.000529 |
| 1993-02-01 | 4 | 0.000423 |
| 2022-06-01 | 4 | 0.000423 |
| 1991-01-21 | 4 | 0.000423 |
| 2002-07-11 | 4 | 0.000423 |
| 2022-09-01 | 4 | 0.000423 |
| 1994-04-01 | 4 | 0.000423 |
| 1994-12-01 | 4 | 0.000423 |
| 1993-03-08 | 4 | 0.000423 |
| 1991-12-01 | 4 | 0.000423 |
| 1992-09-01 | 3 | 0.000317 |
| 1992-10-01 | 3 | 0.000317 |
| 2006-01-01 | 3 | 0.000317 |
| 1993-08-01 | 3 | 0.000317 |
| 1991-07-01 | 3 | 0.000317 |
| 1992-08-14 | 3 | 0.000317 |
| 1996-09-01 | 3 | 0.000317 |
| 2022-07-04 | 3 | 0.000317 |
| 1991-02-20 | 3 | 0.000317 |
| 1993-04-27 | 3 | 0.000317 |
| 1991-09-01 | 3 | 0.000317 |
| 2002-06-18 | 2 | 0.000212 |
| 1991-08-01 | 2 | 0.000212 |
| 2021-05-03 | 2 | 0.000212 |
| 2022-01-03 | 2 | 0.000212 |
| 2023-05-02 | 2 | 0.000212 |
| 1992-02-02 | 2 | 0.000212 |
| 2020-10-05 | 2 | 0.000212 |
| 2023-04-03 | 2 | 0.000212 |
| 2022-04-01 | 2 | 0.000212 |
| 2021-06-01 | 2 | 0.000212 |
| 1994-05-01 | 2 | 0.000212 |
| 1992-08-01 | 2 | 0.000212 |
| 2022-05-02 | 2 | 0.000212 |
| 2021-04-01 | 2 | 0.000212 |
| 1992-06-02 | 2 | 0.000212 |
| 1993-04-01 | 2 | 0.000212 |
| 1992-05-21 | 2 | 0.000212 |
| 1992-09-30 | 2 | 0.000212 |
| 1993-09-01 | 2 | 0.000212 |
| 1991-04-15 | 2 | 0.000212 |
| 1993-03-11 | 2 | 0.000212 |
| 1993-02-12 | 2 | 0.000212 |
| 1991-11-06 | 2 | 0.000212 |
| 1990-10-01 | 1 | 0.000106 |
| 1991-11-01 | 1 | 0.000106 |
| 1990-12-20 | 1 | 0.000106 |
| 1991-01-10 | 1 | 0.000106 |
| 1991-06-03 | 1 | 0.000106 |
| 1997-05-04 | 1 | 0.000106 |
| 1991-10-01 | 1 | 0.000106 |
| 1994-07-28 | 1 | 0.000106 |
| 1992-02-05 | 1 | 0.000106 |
| 1999-09-14 | 1 | 0.000106 |
| 1991-09-03 | 1 | 0.000106 |
| 1993-04-30 | 1 | 0.000106 |
| 1992-03-02 | 1 | 0.000106 |
| 1993-04-19 | 1 | 0.000106 |
| 1991-01-04 | 1 | 0.000106 |
| 1999-08-12 | 1 | 0.000106 |
| 1992-04-01 | 1 | 0.000106 |
| 2023-01-02 | 1 | 0.000106 |
| 2018-10-08 | 1 | 0.000106 |
| 1991-03-25 | 1 | 0.000106 |
| 1993-06-01 | 1 | 0.000106 |
| 1993-05-04 | 1 | 0.000106 |
| 1990-11-01 | 1 | 0.000106 |
| 2021-08-02 | 1 | 0.000106 |
| 1993-04-03 | 1 | 0.000106 |
| 2022-03-09 | 1 | 0.000106 |
| 2002-06-11 | 1 | 0.000106 |
| 2021-12-01 | 1 | 0.000106 |
| 2021-07-01 | 1 | 0.000106 |
| 2022-08-01 | 1 | 0.000106 |
| 2021-04-20 | 1 | 0.000106 |
# Vamos a realizar analisis por cada variable
var = "msf_datelastdonation__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_datelastdonation__c es 726227. Lo que supone un 73.05636256820488% El nº de vacios para la variable msf_datelastdonation__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-07-01 | 6905 | 2.578061 |
| 2022-12-02 | 6763 | 2.525043 |
| 2023-03-02 | 5936 | 2.216273 |
| 2020-06-01 | 3948 | 1.474031 |
| 2021-12-02 | 3672 | 1.370983 |
| ... | ... | ... |
| 2010-05-16 | 1 | 0.000373 |
| 1993-01-29 | 1 | 0.000373 |
| 2011-01-25 | 1 | 0.000373 |
| 2008-08-29 | 1 | 0.000373 |
| 2009-11-15 | 1 | 0.000373 |
8428 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_date__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npsp__largest_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_date__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npsp__first_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_entrydatecurrentrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_entrydatecurrentrecurringdonor__c es 414. Lo que supone un 0.041647217885367536% El nº de vacios para la variable msf_entrydatecurrentrecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2000-02-01 | 3949 | 0.397424 |
| 2004-01-01 | 3842 | 0.386655 |
| 1994-10-01 | 3293 | 0.331404 |
| 2000-01-01 | 3274 | 0.329492 |
| 1995-02-01 | 2918 | 0.293665 |
| ... | ... | ... |
| 2002-01-30 | 1 | 0.000101 |
| 2003-11-17 | 1 | 0.000101 |
| 2005-08-27 | 1 | 0.000101 |
| 2002-01-16 | 1 | 0.000101 |
| 2011-06-25 | 1 | 0.000101 |
7860 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_date__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_date__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npsp__last_soft_credit_date__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_firstentrydaterecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstentrydaterecurringdonor__c es 623. Lo que supone un 0.06267202111735261% El nº de vacios para la variable msf_firstentrydaterecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2004-01-01 | 4974 | 0.500684 |
| 2000-02-01 | 4594 | 0.462433 |
| 1994-10-01 | 3823 | 0.384824 |
| 2000-01-01 | 3804 | 0.382912 |
| 1995-02-01 | 3374 | 0.339628 |
| ... | ... | ... |
| 2003-02-04 | 1 | 0.000101 |
| 2003-07-11 | 1 | 0.000101 |
| 2004-08-12 | 1 | 0.000101 |
| 2003-01-07 | 1 | 0.000101 |
| 2010-04-24 | 1 | 0.000101 |
7926 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__firstclosedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__firstclosedate__c es 57038. Lo que supone un 5.73785993658356% El nº de vacios para la variable npo02__firstclosedate__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2015-12-02 | 50662 | 5.406680 |
| 2016-12-01 | 50659 | 5.406360 |
| 2014-12-02 | 46591 | 4.972221 |
| 2017-12-04 | 45835 | 4.891540 |
| 2013-12-02 | 30543 | 3.259568 |
| ... | ... | ... |
| 1999-02-15 | 1 | 0.000107 |
| 2009-02-21 | 1 | 0.000107 |
| 2001-06-21 | 1 | 0.000107 |
| 2016-09-04 | 1 | 0.000107 |
| 2012-03-05 | 1 | 0.000107 |
7986 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastrecurringdonationdate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastrecurringdonationdate__c es 421682. Lo que supone un 42.42000515057381% El nº de vacios para la variable msf_lastrecurringdonationdate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-03-12 | 2204 | 0.385058 |
| 2014-03-13 | 1942 | 0.339284 |
| 2023-05-10 | 1794 | 0.313427 |
| 2018-03-07 | 1616 | 0.282329 |
| 2018-04-09 | 1555 | 0.271672 |
| ... | ... | ... |
| 2018-08-25 | 1 | 0.000175 |
| 2006-10-22 | 1 | 0.000175 |
| 1993-04-19 | 1 | 0.000175 |
| 2008-04-20 | 1 | 0.000175 |
| 2016-04-09 | 1 | 0.000175 |
7032 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__lastclosedate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastclosedate__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npo02__lastclosedate__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "gender__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable gender__c es 0. Lo que supone un 0.0% El nº de vacios para la variable gender__c es 12555. Lo que supone un 1.2629971510888636%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Female | 548203 | 55.147656 |
| Male | 410016 | 41.246439 |
| Other | 23284 | 2.342304 |
| 12555 | 1.262997 | |
| H | 5 | 0.000503 |
| M | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_languagepreferer__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_languagepreferer__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| ESP | 858589 | 86.371602 |
| CAT | 119327 | 12.003955 |
| GAL | 10821 | 1.088562 |
| EUS | 5317 | 0.534875 |
| ING | 10 | 0.001006 |
# Vamos a realizar analisis por cada variable
var = "npo02__largestamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__largestamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__largestamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 994064 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__smallestamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__smallestamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 994064 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npsp__first_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__first_soft_credit_amount__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npsp__first_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__lastoppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__lastoppamount__c es 3428. Lo que supone un 0.3448470118624153% El nº de vacios para la variable npo02__lastoppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 10.00 | 140562 | 14.189066 |
| 15.00 | 92234 | 9.310584 |
| 20.00 | 74631 | 7.533645 |
| 5.00 | 57681 | 5.822623 |
| 0.00 | 53610 | 5.411675 |
| ... | ... | ... |
| 11250.00 | 1 | 0.000101 |
| 19.16 | 1 | 0.000101 |
| 169685.75 | 1 | 0.000101 |
| 120.08 | 1 | 0.000101 |
| 11.30 | 1 | 0.000101 |
1786 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npsp__last_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__last_soft_credit_amount__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npsp__last_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_annualizedquotachange__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_annualizedquotachange__c es 444680. Lo que supone un 44.733538283249366% El nº de vacios para la variable msf_annualizedquotachange__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 48.00 | 207919 | 37.845842 |
| 24.00 | 46376 | 8.441454 |
| 72.00 | 45925 | 8.359362 |
| 60.00 | 43108 | 7.846606 |
| 120.00 | 28119 | 5.118278 |
| 36.00 | 27732 | 5.047835 |
| 84.00 | 22833 | 4.156109 |
| 50.00 | 10626 | 1.934166 |
| 0.00 | 10586 | 1.926885 |
| 30.00 | 7533 | 1.371172 |
| 144.00 | 7394 | 1.345871 |
| 40.00 | 5521 | 1.004944 |
| 25.00 | 5257 | 0.956890 |
| 45.00 | 4661 | 0.848405 |
| 20.00 | 3932 | 0.715711 |
| 108.00 | 3853 | 0.701331 |
| 28.00 | 3495 | 0.636167 |
| 15.00 | 3448 | 0.627612 |
| 10.00 | 3315 | 0.603403 |
| 64.00 | 3000 | 0.546066 |
| 96.00 | 2767 | 0.503655 |
| 35.88 | 2766 | 0.503473 |
| 35.00 | 2551 | 0.464338 |
| 12.00 | 2207 | 0.401723 |
| 100.00 | 1840 | 0.334921 |
| 70.00 | 1759 | 0.320177 |
| 8.00 | 1751 | 0.318721 |
| 7.00 | 1666 | 0.303249 |
| 5.00 | 1659 | 0.301975 |
| 20.04 | 1625 | 0.295786 |
| 52.00 | 1619 | 0.294694 |
| 90.00 | 1468 | 0.267208 |
| 56.00 | 1443 | 0.262658 |
| 240.00 | 1280 | 0.232988 |
| 6.00 | 1260 | 0.229348 |
| 2.00 | 1217 | 0.221521 |
| 29.90 | 1182 | 0.215150 |
| 18.00 | 1048 | 0.190759 |
| 47.80 | 989 | 0.180020 |
| 80.00 | 966 | 0.175833 |
| 55.00 | 953 | 0.173467 |
| 14.95 | 857 | 0.155993 |
| 132.00 | 853 | 0.155265 |
| 16.00 | 850 | 0.154719 |
| 88.00 | 755 | 0.137427 |
| 44.00 | 714 | 0.129964 |
| 47.76 | 658 | 0.119771 |
| 180.00 | 613 | 0.111580 |
| 65.00 | 613 | 0.111580 |
| 168.00 | 610 | 0.111033 |
| 32.00 | 592 | 0.107757 |
| 76.00 | 574 | 0.104481 |
| 119.40 | 528 | 0.096108 |
| 14.00 | 505 | 0.091921 |
| 17.00 | 503 | 0.091557 |
| 22.00 | 490 | 0.089191 |
| 59.64 | 451 | 0.082092 |
| 33.00 | 448 | 0.081546 |
| 42.00 | 410 | 0.074629 |
| 54.00 | 395 | 0.071899 |
| 44.85 | 388 | 0.070625 |
| 140.00 | 371 | 0.067530 |
| 192.00 | 351 | 0.063890 |
| 71.60 | 334 | 0.060795 |
| 27.00 | 325 | 0.059157 |
| 156.00 | 302 | 0.054971 |
| 4.00 | 301 | 0.054789 |
| 200.00 | 299 | 0.054425 |
| 34.00 | 267 | 0.048600 |
| 160.00 | 263 | 0.047872 |
| 32.88 | 231 | 0.042047 |
| 8.97 | 199 | 0.036222 |
| 11.00 | 198 | 0.036040 |
| 41.00 | 189 | 0.034402 |
| 21.00 | 188 | 0.034220 |
| 68.00 | 188 | 0.034220 |
| 360.00 | 181 | 0.032946 |
| 9.00 | 169 | 0.030762 |
| 44.80 | 167 | 0.030398 |
| 110.00 | 143 | 0.026029 |
| 23.92 | 141 | 0.025665 |
| 130.00 | 136 | 0.024755 |
| 59.75 | 133 | 0.024209 |
| 31.00 | 129 | 0.023481 |
| 300.00 | 126 | 0.022935 |
| 142.80 | 124 | 0.022571 |
| 55.76 | 109 | 0.019840 |
| 480.00 | 107 | 0.019476 |
| 26.00 | 106 | 0.019294 |
| 58.00 | 96 | 0.017474 |
| 3.00 | 96 | 0.017474 |
| 51.96 | 94 | 0.017110 |
| 19.00 | 89 | 0.016200 |
| 62.00 | 86 | 0.015654 |
| 47.88 | 85 | 0.015472 |
| 38.00 | 82 | 0.014926 |
| 17.94 | 79 | 0.014380 |
| 99.40 | 79 | 0.014380 |
| 17.15 | 72 | 0.013106 |
| 40.08 | 70 | 0.012742 |
| 11.96 | 66 | 0.012013 |
| 75.00 | 64 | 0.011649 |
| 104.00 | 59 | 0.010739 |
| 46.00 | 59 | 0.010739 |
| 49.70 | 58 | 0.010557 |
| 52.60 | 55 | 0.010011 |
| 47.84 | 54 | 0.009829 |
| 105.00 | 48 | 0.008737 |
| 89.50 | 46 | 0.008373 |
| 83.52 | 44 | 0.008009 |
| 53.00 | 44 | 0.008009 |
| 400.00 | 40 | 0.007281 |
| 13.00 | 40 | 0.007281 |
| 66.00 | 39 | 0.007099 |
| 46.85 | 36 | 0.006553 |
| 47.00 | 35 | 0.006371 |
| 37.00 | 35 | 0.006371 |
| 600.00 | 33 | 0.006007 |
| 35.76 | 33 | 0.006007 |
| 5.98 | 32 | 0.005825 |
| 95.00 | 31 | 0.005643 |
| 32.04 | 30 | 0.005461 |
| 43.00 | 29 | 0.005279 |
| 720.00 | 28 | 0.005097 |
| 49.00 | 26 | 0.004733 |
| 2.99 | 25 | 0.004551 |
| 51.00 | 24 | 0.004369 |
| 150.00 | 24 | 0.004369 |
| 35.88 | 22 | 0.004004 |
| 119.00 | 22 | 0.004004 |
| 15.96 | 22 | 0.004004 |
| 66.96 | 21 | 0.003822 |
| 63.72 | 20 | 0.003640 |
| 125.00 | 20 | 0.003640 |
| 178.20 | 20 | 0.003640 |
| 119.28 | 20 | 0.003640 |
| 55.68 | 19 | 0.003458 |
| 78.00 | 19 | 0.003458 |
| 320.00 | 18 | 0.003276 |
| 1200.00 | 17 | 0.003094 |
| 27.92 | 17 | 0.003094 |
| 139.20 | 17 | 0.003094 |
| 92.00 | 16 | 0.002912 |
| 118.99 | 16 | 0.002912 |
| 85.00 | 15 | 0.002730 |
| 112.00 | 15 | 0.002730 |
| 63.64 | 14 | 0.002548 |
| 121.80 | 14 | 0.002548 |
| 51.72 | 14 | 0.002548 |
| 228.00 | 13 | 0.002366 |
| 26.91 | 12 | 0.002184 |
| 40.86 | 12 | 0.002184 |
| 59.00 | 12 | 0.002184 |
| 107.04 | 11 | 0.002002 |
| 280.00 | 11 | 0.002002 |
| 107.40 | 11 | 0.002002 |
| 14.16 | 11 | 0.002002 |
| 118.56 | 10 | 0.001820 |
| 57.00 | 10 | 0.001820 |
| 166.56 | 10 | 0.001820 |
| 69.60 | 9 | 0.001638 |
| 36.87 | 9 | 0.001638 |
| 51.82 | 9 | 0.001638 |
| 29.00 | 8 | 0.001456 |
| 124.00 | 8 | 0.001456 |
| 56.64 | 8 | 0.001456 |
| 46.56 | 8 | 0.001456 |
| 20.93 | 8 | 0.001456 |
| 39.00 | 8 | 0.001456 |
| 71.64 | 7 | 0.001274 |
| 23.00 | 7 | 0.001274 |
| 237.60 | 7 | 0.001274 |
| 95.16 | 7 | 0.001274 |
| 119.40 | 6 | 0.001092 |
| 28.31 | 6 | 0.001092 |
| 39.88 | 6 | 0.001092 |
| 216.00 | 6 | 0.001092 |
| 29.76 | 6 | 0.001092 |
| 45.60 | 6 | 0.001092 |
| 420.00 | 6 | 0.001092 |
| 204.00 | 6 | 0.001092 |
| 115.00 | 5 | 0.000910 |
| 276.00 | 5 | 0.000910 |
| 960.00 | 5 | 0.000910 |
| 47.52 | 5 | 0.000910 |
| 116.00 | 5 | 0.000910 |
| 357.00 | 5 | 0.000910 |
| 59.64 | 5 | 0.000910 |
| 51.80 | 5 | 0.000910 |
| 1440.00 | 5 | 0.000910 |
| 126.00 | 5 | 0.000910 |
| 89.49 | 5 | 0.000910 |
| 94.00 | 5 | 0.000910 |
| 26.32 | 5 | 0.000910 |
| 220.00 | 4 | 0.000728 |
| 238.00 | 4 | 0.000728 |
| 97.92 | 4 | 0.000728 |
| 8.97 | 4 | 0.000728 |
| 19.95 | 4 | 0.000728 |
| 74.00 | 4 | 0.000728 |
| 135.00 | 4 | 0.000728 |
| 51.84 | 4 | 0.000728 |
| 6.58 | 4 | 0.000728 |
| 79.50 | 4 | 0.000728 |
| 47.64 | 4 | 0.000728 |
| 1000.00 | 4 | 0.000728 |
| 114.00 | 4 | 0.000728 |
| 63.00 | 4 | 0.000728 |
| 21.93 | 4 | 0.000728 |
| 47.76 | 4 | 0.000728 |
| 59.65 | 4 | 0.000728 |
| 44.64 | 4 | 0.000728 |
| 41.88 | 4 | 0.000728 |
| 67.00 | 4 | 0.000728 |
| 68.04 | 3 | 0.000546 |
| 714.00 | 3 | 0.000546 |
| 21.60 | 3 | 0.000546 |
| 16.80 | 3 | 0.000546 |
| 14.88 | 3 | 0.000546 |
| 260.00 | 3 | 0.000546 |
| 2400.00 | 3 | 0.000546 |
| 34.90 | 3 | 0.000546 |
| 106.00 | 3 | 0.000546 |
| 33.89 | 3 | 0.000546 |
| 128.00 | 3 | 0.000546 |
| 800.00 | 3 | 0.000546 |
| 82.00 | 3 | 0.000546 |
| 136.00 | 3 | 0.000546 |
| 25.04 | 3 | 0.000546 |
| 49.85 | 3 | 0.000546 |
| 129.25 | 3 | 0.000546 |
| 16.95 | 3 | 0.000546 |
| 59.76 | 3 | 0.000546 |
| 148.00 | 3 | 0.000546 |
| 55.92 | 2 | 0.000364 |
| 53.82 | 2 | 0.000364 |
| 145.00 | 2 | 0.000364 |
| 3.99 | 2 | 0.000364 |
| 780.00 | 2 | 0.000364 |
| 31.90 | 2 | 0.000364 |
| 17.34 | 2 | 0.000364 |
| 3.34 | 2 | 0.000364 |
| 32.88 | 2 | 0.000364 |
| 60.60 | 2 | 0.000364 |
| 38.76 | 2 | 0.000364 |
| 162.00 | 2 | 0.000364 |
| 75.60 | 2 | 0.000364 |
| 118.80 | 2 | 0.000364 |
| 13.96 | 2 | 0.000364 |
| 500.00 | 2 | 0.000364 |
| 356.40 | 2 | 0.000364 |
| 6.68 | 2 | 0.000364 |
| 83.00 | 2 | 0.000364 |
| 64.08 | 2 | 0.000364 |
| 14400.00 | 2 | 0.000364 |
| 288.00 | 2 | 0.000364 |
| 20.95 | 2 | 0.000364 |
| 43.88 | 2 | 0.000364 |
| 44.04 | 2 | 0.000364 |
| 74.04 | 2 | 0.000364 |
| 102.00 | 2 | 0.000364 |
| 33.48 | 2 | 0.000364 |
| 324.00 | 2 | 0.000364 |
| 44.90 | 2 | 0.000364 |
| 24.12 | 2 | 0.000364 |
| 540.00 | 2 | 0.000364 |
| 122.40 | 1 | 0.000182 |
| 35.76 | 1 | 0.000182 |
| 28.80 | 1 | 0.000182 |
| 264.00 | 1 | 0.000182 |
| 100.08 | 1 | 0.000182 |
| 13.60 | 1 | 0.000182 |
| 115.20 | 1 | 0.000182 |
| 83.60 | 1 | 0.000182 |
| 840.00 | 1 | 0.000182 |
| 154.00 | 1 | 0.000182 |
| 5.50 | 1 | 0.000182 |
| 63.60 | 1 | 0.000182 |
| 141.12 | 1 | 0.000182 |
| 384.00 | 1 | 0.000182 |
| 580.00 | 1 | 0.000182 |
| 86.00 | 1 | 0.000182 |
| 33.90 | 1 | 0.000182 |
| -720.00 | 1 | 0.000182 |
| 59.28 | 1 | 0.000182 |
| -96.00 | 1 | 0.000182 |
| 143.76 | 1 | 0.000182 |
| 39.60 | 1 | 0.000182 |
| 43.84 | 1 | 0.000182 |
| 29.85 | 1 | 0.000182 |
| 97.00 | 1 | 0.000182 |
| 4.50 | 1 | 0.000182 |
| 202.20 | 1 | 0.000182 |
| 41.16 | 1 | 0.000182 |
| 440.00 | 1 | 0.000182 |
| 87.00 | 1 | 0.000182 |
| 34.99 | 1 | 0.000182 |
| 14.50 | 1 | 0.000182 |
| 475.20 | 1 | 0.000182 |
| 6.98 | 1 | 0.000182 |
| 81.52 | 1 | 0.000182 |
| 71.76 | 1 | 0.000182 |
| 51.96 | 1 | 0.000182 |
| 55.68 | 1 | 0.000182 |
| 44.25 | 1 | 0.000182 |
| 60.48 | 1 | 0.000182 |
| 51.77 | 1 | 0.000182 |
| 59.70 | 1 | 0.000182 |
| 29.95 | 1 | 0.000182 |
| 65.76 | 1 | 0.000182 |
| 3600.00 | 1 | 0.000182 |
| 81.12 | 1 | 0.000182 |
| 138.00 | 1 | 0.000182 |
| 51.85 | 1 | 0.000182 |
| 190.80 | 1 | 0.000182 |
| 107.40 | 1 | 0.000182 |
| 237.96 | 1 | 0.000182 |
| 560.00 | 1 | 0.000182 |
| 277.60 | 1 | 0.000182 |
| 8.66 | 1 | 0.000182 |
| 49.85 | 1 | 0.000182 |
| 1920.00 | 1 | 0.000182 |
| 49.90 | 1 | 0.000182 |
| 80.04 | 1 | 0.000182 |
| 296.97 | 1 | 0.000182 |
| 52.64 | 1 | 0.000182 |
| 135.44 | 1 | 0.000182 |
| 64.65 | 1 | 0.000182 |
| 27.88 | 1 | 0.000182 |
| 60.04 | 1 | 0.000182 |
| 41.86 | 1 | 0.000182 |
| 28.68 | 1 | 0.000182 |
| 17.95 | 1 | 0.000182 |
| -192.00 | 1 | 0.000182 |
| 109.25 | 1 | 0.000182 |
| 52.78 | 1 | 0.000182 |
| 64.32 | 1 | 0.000182 |
| 900.00 | 1 | 0.000182 |
| 59.85 | 1 | 0.000182 |
| -168.00 | 1 | 0.000182 |
| 43.86 | 1 | 0.000182 |
| 29.99 | 1 | 0.000182 |
| 713.88 | 1 | 0.000182 |
| 32.10 | 1 | 0.000182 |
| 59.80 | 1 | 0.000182 |
| 127.28 | 1 | 0.000182 |
| 131.88 | 1 | 0.000182 |
| 83.49 | 1 | 0.000182 |
| 57.36 | 1 | 0.000182 |
| 48.85 | 1 | 0.000182 |
| 47.83 | 1 | 0.000182 |
| 44.85 | 1 | 0.000182 |
| 179.00 | 1 | 0.000182 |
| 28.98 | 1 | 0.000182 |
| 24000.00 | 1 | 0.000182 |
| 139.00 | 1 | 0.000182 |
| 91.92 | 1 | 0.000182 |
| 178.80 | 1 | 0.000182 |
| 348.00 | 1 | 0.000182 |
| 16.08 | 1 | 0.000182 |
| 297.47 | 1 | 0.000182 |
| 166.68 | 1 | 0.000182 |
| 55.80 | 1 | 0.000182 |
| 77.00 | 1 | 0.000182 |
| 32.28 | 1 | 0.000182 |
| 67.76 | 1 | 0.000182 |
| 17.94 | 1 | 0.000182 |
| -108.00 | 1 | 0.000182 |
| 28.08 | 1 | 0.000182 |
| 52.08 | 1 | 0.000182 |
| 33.04 | 1 | 0.000182 |
| 32.14 | 1 | 0.000182 |
| 165.12 | 1 | 0.000182 |
| 432.00 | 1 | 0.000182 |
| 250.00 | 1 | 0.000182 |
| 15.92 | 1 | 0.000182 |
| 98.56 | 1 | 0.000182 |
| 129.00 | 1 | 0.000182 |
| 63.24 | 1 | 0.000182 |
| 71.88 | 1 | 0.000182 |
| 50.68 | 1 | 0.000182 |
| 61.68 | 1 | 0.000182 |
| 103.44 | 1 | 0.000182 |
| 119.20 | 1 | 0.000182 |
| 5.60 | 1 | 0.000182 |
| 297.60 | 1 | 0.000182 |
| 122.00 | 1 | 0.000182 |
| 87.52 | 1 | 0.000182 |
| 20.40 | 1 | 0.000182 |
| 143.40 | 1 | 0.000182 |
| 158.60 | 1 | 0.000182 |
| 236.00 | 1 | 0.000182 |
| 81.00 | 1 | 0.000182 |
| 52.88 | 1 | 0.000182 |
| 16.44 | 1 | 0.000182 |
| 56.04 | 1 | 0.000182 |
| 52.68 | 1 | 0.000182 |
| 35.40 | 1 | 0.000182 |
| 24.60 | 1 | 0.000182 |
| 257.57 | 1 | 0.000182 |
| 660.00 | 1 | 0.000182 |
| 52.80 | 1 | 0.000182 |
| 1600.00 | 1 | 0.000182 |
| 372.00 | 1 | 0.000182 |
| 69.00 | 1 | 0.000182 |
| 640.00 | 1 | 0.000182 |
| 53.64 | 1 | 0.000182 |
| 44.40 | 1 | 0.000182 |
| 67.64 | 1 | 0.000182 |
| 38.28 | 1 | 0.000182 |
| 350.00 | 1 | 0.000182 |
| 43.08 | 1 | 0.000182 |
| 10.50 | 1 | 0.000182 |
| 700.00 | 1 | 0.000182 |
| 37.90 | 1 | 0.000182 |
| 19.80 | 1 | 0.000182 |
| 63.76 | 1 | 0.000182 |
| 61.00 | 1 | 0.000182 |
| 63.80 | 1 | 0.000182 |
| 109.92 | 1 | 0.000182 |
| 15.95 | 1 | 0.000182 |
| 127.28 | 1 | 0.000182 |
| 147.72 | 1 | 0.000182 |
| 19.94 | 1 | 0.000182 |
| 64.75 | 1 | 0.000182 |
| 133.36 | 1 | 0.000182 |
| 1320.00 | 1 | 0.000182 |
| 66.84 | 1 | 0.000182 |
| 40.68 | 1 | 0.000182 |
| 197.98 | 1 | 0.000182 |
| 89.00 | 1 | 0.000182 |
# Vamos a realizar analisis por cada variable
var = "msf_relationshiplevel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_relationshiplevel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_relationshiplevel__c es 4. Lo que supone un 0.00040238857860258495%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| a0l0O00000k727RQAQ | 942828 | 94.845805 |
| a0l0O00000k727QQAQ | 27803 | 2.796902 |
| a0l0O00000k727SQAQ | 17898 | 1.800488 |
| a0l0O00000k727TQAQ | 5328 | 0.535982 |
| a0l0O00000k727UQAQ | 203 | 0.020421 |
| 4 | 0.000402 |
# Vamos a realizar analisis por cada variable
var = "msf_ltvcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_ltvcont__c es 57038. Lo que supone un 5.73785993658356% El nº de vacios para la variable msf_ltvcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 30.00 | 12590 | 1.343613 |
| 60.00 | 12133 | 1.294841 |
| 10.00 | 11459 | 1.222912 |
| 20.00 | 11239 | 1.199433 |
| 40.00 | 8630 | 0.920999 |
| ... | ... | ... |
| 1769.28 | 1 | 0.000107 |
| 825.43 | 1 | 0.000107 |
| 3180.61 | 1 | 0.000107 |
| 7776.81 | 1 | 0.000107 |
| 1628.70 | 1 | 0.000107 |
83992 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "mailingstate"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable mailingstate es 0. Lo que supone un 0.0% El nº de vacios para la variable mailingstate es 53511. Lo que supone un 5.383053807400731%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| MADRID | 161171 | 16.213342 |
| BARCELONA | 120407 | 12.112600 |
| 53511 | 5.383054 | |
| VALENCIA/VALÈNCIA | 50993 | 5.129750 |
| BIZKAIA | 38416 | 3.864540 |
| SEVILLA | 29253 | 2.942768 |
| MÁLAGA | 29229 | 2.940354 |
| ALICANTE/ALACANT | 28276 | 2.844485 |
| A CORUÑA | 26069 | 2.622467 |
| GIPUZKOA | 25125 | 2.527503 |
| ILLES BALEARS | 23165 | 2.330333 |
| PONTEVEDRA | 20583 | 2.070591 |
| MURCIA | 19573 | 1.968988 |
| ASTURIAS | 17610 | 1.771516 |
| SANTA CRUZ DE TENERIFE | 17540 | 1.764474 |
| CÁDIZ | 16967 | 1.706832 |
| LAS PALMAS | 16013 | 1.610862 |
| GRANADA | 13940 | 1.402324 |
| Madrid | 13426 | 1.350617 |
| ZARAGOZA | 13156 | 1.323456 |
| GIRONA | 11384 | 1.145198 |
| NAVARRA | 10910 | 1.097515 |
| Barcelona | 10571 | 1.063412 |
| CANTABRIA | 10477 | 1.053956 |
| TARRAGONA | 9630 | 0.968751 |
| VALLADOLID | 9453 | 0.950945 |
| ARABA/ÁLAVA | 9312 | 0.936761 |
| CASTELLÓN/CASTELLÓ | 8882 | 0.893504 |
| CÓRDOBA | 7210 | 0.725305 |
| TOLEDO | 6862 | 0.690298 |
| LEÓN | 6798 | 0.683859 |
| HUELVA | 6142 | 0.617868 |
| BURGOS | 5521 | 0.555397 |
| BADAJOZ | 5436 | 0.546846 |
| LLEIDA | 5309 | 0.534070 |
| Vizcaya | 5246 | 0.527733 |
| CIUDAD REAL | 5102 | 0.513247 |
| Alicante/Alacant | 5095 | 0.512542 |
| ALMERÍA | 4785 | 0.481357 |
| JAÉN | 4694 | 0.472203 |
| LA RIOJA | 4550 | 0.457717 |
| GUADALAJARA | 4354 | 0.438000 |
| Murcia | 4282 | 0.430757 |
| Sevilla | 4129 | 0.415366 |
| SALAMANCA | 3971 | 0.399471 |
| CÁCERES | 3951 | 0.397459 |
| OURENSE | 3628 | 0.364966 |
| ALBACETE | 3616 | 0.363759 |
| LUGO | 3578 | 0.359937 |
| Malaga | 3569 | 0.359031 |
| Granada | 3556 | 0.357723 |
| Valencia/Valencia | 3480 | 0.350078 |
| Cadiz | 3064 | 0.308230 |
| HUESCA | 2838 | 0.285495 |
| A Coruña | 2710 | 0.272618 |
| Valencia | 2437 | 0.245155 |
| SEGOVIA | 2280 | 0.229361 |
| Santa Cruz de Tenerife | 2164 | 0.217692 |
| PALENCIA | 2015 | 0.202703 |
| CUENCA | 1942 | 0.195360 |
| ÁVILA | 1926 | 0.193750 |
| ZAMORA | 1875 | 0.188620 |
| Alicante | 1617 | 0.162666 |
| Guipuzcoa | 1582 | 0.159145 |
| SORIA | 1423 | 0.143150 |
| Málaga | 1378 | 0.138623 |
| Badajoz | 1316 | 0.132386 |
| TERUEL | 1272 | 0.127960 |
| Salamanca | 1258 | 0.126551 |
| Pontevedra | 1048 | 0.105426 |
| Cádiz | 1033 | 0.103917 |
| VALENCIA | 956 | 0.096171 |
| Asturias | 941 | 0.094662 |
| Almeria | 903 | 0.090839 |
| Illes Balears | 899 | 0.090437 |
| Ciudad Real | 845 | 0.085005 |
| Las Palmas | 841 | 0.084602 |
| Navarra | 824 | 0.082892 |
| Cantabria | 747 | 0.075146 |
| Bizkaia | 736 | 0.074039 |
| MELILLA | 720 | 0.072430 |
| MALAGA | 707 | 0.071122 |
| Huelva | 689 | 0.069311 |
| Tarragona | 687 | 0.069110 |
| CEUTA | 674 | 0.067802 |
| Zaragoza | 673 | 0.067702 |
| Girona | 668 | 0.067199 |
| Valladolid | 536 | 0.053920 |
| ALICANTE | 535 | 0.053819 |
| Toledo | 494 | 0.049695 |
| Santa Cruz De Tenerife | 488 | 0.049091 |
| Valencia/València | 457 | 0.045973 |
| Almería | 442 | 0.044464 |
| Caceres | 429 | 0.043156 |
| Valencia/Valéncia | 421 | 0.042351 |
| Baleares | 418 | 0.042050 |
| Gipuzkoa | 406 | 0.040842 |
| Guipúzcoa | 406 | 0.040842 |
| Burgos | 402 | 0.040440 |
| Castellon/Castello | 360 | 0.036215 |
| VIZCAYA | 343 | 0.034505 |
| Cordoba | 327 | 0.032895 |
| CADIZ | 319 | 0.032090 |
| Guadalajara | 298 | 0.029978 |
| Lleida | 297 | 0.029877 |
| Albacete | 295 | 0.029676 |
| La Rioja | 294 | 0.029576 |
| Jaen | 291 | 0.029274 |
| Lugo | 284 | 0.028570 |
| Cáceres | 242 | 0.024345 |
| Ourense | 221 | 0.022232 |
| alava | 219 | 0.022031 |
| Córdoba | 214 | 0.021528 |
| León | 207 | 0.020824 |
| Huesca | 201 | 0.020220 |
| Leon | 195 | 0.019616 |
| Castellon | 190 | 0.019113 |
| Segovia | 189 | 0.019013 |
| Jaén | 179 | 0.018007 |
| Zamora | 175 | 0.017605 |
| Castellón | 168 | 0.016900 |
| GUIPUZCOA | 155 | 0.015593 |
| Alava | 150 | 0.015090 |
| CASTELLON | 148 | 0.014888 |
| LEON | 143 | 0.014385 |
| Alacant | 142 | 0.014285 |
| MAlaga | 133 | 0.013379 |
| CORDOBA | 133 | 0.013379 |
| CAdiz | 130 | 0.013078 |
| Álava | 129 | 0.012977 |
| València | 124 | 0.012474 |
| Castellón/Castelló | 122 | 0.012273 |
| Melilla | 122 | 0.012273 |
| Cuenca | 122 | 0.012273 |
| ALMERIA | 114 | 0.011468 |
| Valencia/ValEncia | 113 | 0.011367 |
| JAEN | 109 | 0.010965 |
| Palencia | 105 | 0.010563 |
| Araba/Alava | 91 | 0.009154 |
| ALAVA | 82 | 0.008249 |
| Teruel | 81 | 0.008148 |
| Tenerife | 80 | 0.008048 |
| CACERES | 75 | 0.007545 |
| TENERIFE | 72 | 0.007243 |
| Soria | 71 | 0.007142 |
| Ávila | 64 | 0.006438 |
| GuipUzcoa | 63 | 0.006338 |
| BALEARES | 61 | 0.006136 |
| Ceuta | 61 | 0.006136 |
| madrid | 59 | 0.005935 |
| VALENCIA/VALéNCIA | 53 | 0.005332 |
| ISLAS BALEARES | 51 | 0.005130 |
| BILBAO | 47 | 0.004728 |
| VALENCIA/VALÉNCIA | 47 | 0.004728 |
| AVILA | 43 | 0.004326 |
| Guipuzkoa | 40 | 0.004024 |
| avila | 38 | 0.003823 |
| MALLORCA | 37 | 0.003722 |
| LAS PALMAS DE GRAN CANARIA | 36 | 0.003621 |
| CORUÑA | 34 | 0.003420 |
| Islas Baleares | 33 | 0.003320 |
| Avila | 33 | 0.003320 |
| GERONA | 32 | 0.003219 |
| CANARIAS | 32 | 0.003219 |
| GRAN CANARIA | 30 | 0.003018 |
| CAceres | 28 | 0.002817 |
| LA CORUÑA | 27 | 0.002716 |
| ORENSE | 25 | 0.002515 |
| A Coru?a | 25 | 0.002515 |
| Bilbao | 24 | 0.002414 |
| Valencia/Valéncia | 23 | 0.002314 |
| barcelona | 23 | 0.002314 |
| CastellOn/CastellO | 22 | 0.002213 |
| VIGO | 21 | 0.002113 |
| PALMA DE MALLORCA | 19 | 0.001911 |
| MaLAGA | 19 | 0.001911 |
| CaDIZ | 18 | 0.001811 |
| malaga | 18 | 0.001811 |
| valencia | 17 | 0.001710 |
| GALICIA | 17 | 0.001710 |
| AlmerIa | 17 | 0.001710 |
| Castelló | 16 | 0.001610 |
| Málaga | 16 | 0.001610 |
| Bizcaia | 16 | 0.001610 |
| alicante | 15 | 0.001509 |
| LERIDA | 15 | 0.001509 |
| PAMPLONA | 14 | 0.001408 |
| IBIZA | 14 | 0.001408 |
| sevilla | 13 | 0.001308 |
| M?laga | 13 | 0.001308 |
| GUIPUZCUA | 13 | 0.001308 |
| Guipuzcua | 13 | 0.001308 |
| GUIPUZKOA | 13 | 0.001308 |
| SevillA | 13 | 0.001308 |
| ARABA/ALAVA | 12 | 0.001207 |
| ALACANT | 12 | 0.001207 |
| CASTELLÓN | 12 | 0.001207 |
| OVIEDO | 12 | 0.001207 |
| Las Palmas de Gran Canarias | 11 | 0.001107 |
| MadrId | 11 | 0.001107 |
| LANZAROTE | 11 | 0.001107 |
| VIZCAIA | 10 | 0.001006 |
| JaEn | 10 | 0.001006 |
| SANTA CRUZ TENERIFE | 10 | 0.001006 |
| LAS PALMAS DE GRAN CANARIAS | 10 | 0.001006 |
| COrdoba | 10 | 0.001006 |
| GUIPÚZCOA | 9 | 0.000905 |
| ARABA | 9 | 0.000905 |
| cadiz | 9 | 0.000905 |
| CáDIZ | 9 | 0.000905 |
| SANTANDER | 9 | 0.000905 |
| Guipúzcoa | 9 | 0.000905 |
| Gerona | 9 | 0.000905 |
| Araba/Álava | 9 | 0.000905 |
| Orense | 8 | 0.000805 |
| badajoz | 8 | 0.000805 |
| AlIcante/Alacant | 8 | 0.000805 |
| BArcelonA | 8 | 0.000805 |
| asturias | 8 | 0.000805 |
| Santander | 8 | 0.000805 |
| Vizkaya | 8 | 0.000805 |
| Bizcaya | 8 | 0.000805 |
| murcia | 8 | 0.000805 |
| ILLES BALEARES | 8 | 0.000805 |
| SAN SEBASTIAN | 8 | 0.000805 |
| salamanca | 7 | 0.000704 |
| VIZKAYA | 7 | 0.000704 |
| GIJON | 7 | 0.000704 |
| Coruña | 7 | 0.000704 |
| LeOn | 7 | 0.000704 |
| ÁLAVA | 7 | 0.000704 |
| A coruña | 7 | 0.000704 |
| CASTELLON/CASTELLO | 7 | 0.000704 |
| La Coruña | 7 | 0.000704 |
| CORUÑA,A | 7 | 0.000704 |
| MENORCA | 7 | 0.000704 |
| MurcIa | 7 | 0.000704 |
| toledo | 7 | 0.000704 |
| MAlAgA | 7 | 0.000704 |
| Mallorca | 7 | 0.000704 |
| Las Palmas De Gran Canaria | 6 | 0.000604 |
| ValencIa | 6 | 0.000604 |
| Araba | 6 | 0.000604 |
| A CORU?A | 6 | 0.000604 |
| pontevedra | 6 | 0.000604 |
| A Coruña | 6 | 0.000604 |
| Santa Cruz de TenerIfe | 6 | 0.000604 |
| vizcaya | 6 | 0.000604 |
| BIZCAIA | 6 | 0.000604 |
| Gran Canaria | 6 | 0.000604 |
| Las Palmas de Gran Canaria | 6 | 0.000604 |
| C?diz | 6 | 0.000604 |
| a coruña | 6 | 0.000604 |
| Vigo | 5 | 0.000503 |
| VITORIA | 5 | 0.000503 |
| CORUÑA, A | 5 | 0.000503 |
| LOGROÑO | 5 | 0.000503 |
| CASTELLON DE LA PLANA | 5 | 0.000503 |
| Illes Baleares | 5 | 0.000503 |
| MAdrid | 5 | 0.000503 |
| Cádiz | 5 | 0.000503 |
| Valencia/valència | 5 | 0.000503 |
| PAIS VASCO | 5 | 0.000503 |
| valladolid | 5 | 0.000503 |
| Cartagena | 5 | 0.000503 |
| Gipuzcoa | 5 | 0.000503 |
| Almer?a | 5 | 0.000503 |
| LA CORUNA | 5 | 0.000503 |
| ANDORRA | 5 | 0.000503 |
| BIZCAYA | 4 | 0.000402 |
| Alicante/alacant | 4 | 0.000402 |
| ValencIa/ValencIa | 4 | 0.000402 |
| santa cruz de tenerife | 4 | 0.000402 |
| CaCERES | 4 | 0.000402 |
| ISLAS CANARIAS | 4 | 0.000402 |
| CORU?A | 4 | 0.000402 |
| Logroño | 4 | 0.000402 |
| FRANCIA | 4 | 0.000402 |
| Palma De Mallorca | 4 | 0.000402 |
| SevIlla | 4 | 0.000402 |
| Guip?zcoa | 4 | 0.000402 |
| Guipuzkua | 4 | 0.000402 |
| C?ceres | 4 | 0.000402 |
| Oviedo | 4 | 0.000402 |
| CáCERES | 4 | 0.000402 |
| VAlenciA/VAlenciA | 4 | 0.000402 |
| Canarias | 4 | 0.000402 |
| LEoN | 4 | 0.000402 |
| Le?n | 4 | 0.000402 |
| Castell?n | 4 | 0.000402 |
| CARTAGENA | 4 | 0.000402 |
| GUIPUZKUA | 4 | 0.000402 |
| illes balears | 4 | 0.000402 |
| segovia | 4 | 0.000402 |
| GuIpuzcoa | 4 | 0.000402 |
| Lanzarote | 4 | 0.000402 |
| PALMA | 4 | 0.000402 |
| Pamplona | 4 | 0.000402 |
| CastellOn | 3 | 0.000302 |
| caceres | 3 | 0.000302 |
| VizcAyA | 3 | 0.000302 |
| DONOSTIA | 3 | 0.000302 |
| Asturia | 3 | 0.000302 |
| CASTILLA Y LEON | 3 | 0.000302 |
| jaen | 3 | 0.000302 |
| C?rdoba | 3 | 0.000302 |
| girona | 3 | 0.000302 |
| CASTELLoN/CASTELLo | 3 | 0.000302 |
| Hessen | 3 | 0.000302 |
| SANTIAGO DE COMPOSTELA | 3 | 0.000302 |
| Alacant / Alicante | 3 | 0.000302 |
| Malága | 3 | 0.000302 |
| cantabria | 3 | 0.000302 |
| MáLAGA | 3 | 0.000302 |
| ALEMANIA | 3 | 0.000302 |
| JAeN | 3 | 0.000302 |
| cordoba | 3 | 0.000302 |
| STA. CRUZ DE TENERIFE | 3 | 0.000302 |
| bizkaia | 3 | 0.000302 |
| CIUDAD | 3 | 0.000302 |
| Albecete | 3 | 0.000302 |
| DONOSTI | 3 | 0.000302 |
| ALABA | 3 | 0.000302 |
| CoRDOBA | 3 | 0.000302 |
| lugo | 3 | 0.000302 |
| VALENCIANA | 3 | 0.000302 |
| AlicAnte/AlAcAnt | 3 | 0.000302 |
| LA PALMA | 3 | 0.000302 |
| Santa Cruz Tenerife | 3 | 0.000302 |
| CIudad Real | 3 | 0.000302 |
| ?lava | 3 | 0.000302 |
| Las Palmas De Gran Canarias | 3 | 0.000302 |
| burgos | 3 | 0.000302 |
| ZAGAROZA | 2 | 0.000201 |
| POTEVEDRA | 2 | 0.000201 |
| Fuerteventura | 2 | 0.000201 |
| Guipizcoa | 2 | 0.000201 |
| VIzcaya | 2 | 0.000201 |
| LA PALMAS DE GRAN CANARIA | 2 | 0.000201 |
| PALMA MALLORCA | 2 | 0.000201 |
| Cáceres | 2 | 0.000201 |
| Gijón | 2 | 0.000201 |
| CadIz | 2 | 0.000201 |
| Guizpuzcoa | 2 | 0.000201 |
| Balears | 2 | 0.000201 |
| Marbella | 2 | 0.000201 |
| GRAN CANARIAS | 2 | 0.000201 |
| LORCA | 2 | 0.000201 |
| RIOJA,LA | 2 | 0.000201 |
| BRETAÑA | 2 | 0.000201 |
| EXTREMADURA | 2 | 0.000201 |
| BALEARES, ISLAS | 2 | 0.000201 |
| Sud-Kivu | 2 | 0.000201 |
| ESPAÑA | 2 | 0.000201 |
| SAN CRUZ DE TENERIFE | 2 | 0.000201 |
| tenerife | 2 | 0.000201 |
| almeria | 2 | 0.000201 |
| Alicante/Alacantt | 2 | 0.000201 |
| GUIPUZ | 2 | 0.000201 |
| Bruxelles | 2 | 0.000201 |
| Zaragoz | 2 | 0.000201 |
| SEVILA | 2 | 0.000201 |
| STA CRUZ DE TENERIFE | 2 | 0.000201 |
| mallorca | 2 | 0.000201 |
| BALEARS | 2 | 0.000201 |
| CASTELLÓ | 2 | 0.000201 |
| Palma de Mallorca | 2 | 0.000201 |
| MARBELLA | 2 | 0.000201 |
| VALLLADOLID | 2 | 0.000201 |
| ASTURIA | 2 | 0.000201 |
| navarra | 2 | 0.000201 |
| BAJADOZ | 2 | 0.000201 |
| Portugal | 2 | 0.000201 |
| Valencai | 2 | 0.000201 |
| aLAVA | 2 | 0.000201 |
| zamora | 2 | 0.000201 |
| tarragona | 2 | 0.000201 |
| GRANADILLA DE ABONA | 2 | 0.000201 |
| Vizkaia | 2 | 0.000201 |
| bilbao | 2 | 0.000201 |
| València/Valencia | 2 | 0.000201 |
| Galicia | 2 | 0.000201 |
| VITORIA-GASTEIZ | 2 | 0.000201 |
| Elche | 2 | 0.000201 |
| GrAnAdA | 2 | 0.000201 |
| Gipozkoa | 2 | 0.000201 |
| Vitoria | 2 | 0.000201 |
| SALAMNCA | 2 | 0.000201 |
| las palmas | 2 | 0.000201 |
| Valladolidad | 2 | 0.000201 |
| EXTRANJERO | 2 | 0.000201 |
| ACORUÑA | 2 | 0.000201 |
| Castellon De La Plana | 2 | 0.000201 |
| zaragoza | 2 | 0.000201 |
| Islas Canarias | 2 | 0.000201 |
| CANARIA | 2 | 0.000201 |
| TARRRAGONA | 2 | 0.000201 |
| SALMANCA | 2 | 0.000201 |
| SANTA CRUZ | 2 | 0.000201 |
| granada | 2 | 0.000201 |
| Illes Balers | 2 | 0.000201 |
| LLeida | 2 | 0.000201 |
| Asturies | 2 | 0.000201 |
| GIPUZCOA | 2 | 0.000201 |
| Zaragona | 2 | 0.000201 |
| ARABA/aLAVA | 2 | 0.000201 |
| Santa cruz de Tenerife | 2 | 0.000201 |
| Ja?n | 1 | 0.000101 |
| VALENCIA/VALENCIA | 1 | 0.000101 |
| Bizakia | 1 | 0.000101 |
| Alemania | 1 | 0.000101 |
| Kadiogo | 1 | 0.000101 |
| Seilla | 1 | 0.000101 |
| SANTANDER(CANTABRIA) | 1 | 0.000101 |
| ABACETE | 1 | 0.000101 |
| ciudad real | 1 | 0.000101 |
| Murcio | 1 | 0.000101 |
| VICAYA | 1 | 0.000101 |
| LA RiojA | 1 | 0.000101 |
| A CoruñA | 1 | 0.000101 |
| Araba/alava | 1 | 0.000101 |
| Gelderland | 1 | 0.000101 |
| VILLA NUEVA DE FRESNO | 1 | 0.000101 |
| ILLESBALEARS | 1 | 0.000101 |
| Servilla | 1 | 0.000101 |
| A CORUÑA | 1 | 0.000101 |
| SANTACRUZ DE TENERIFE | 1 | 0.000101 |
| Roma | 1 | 0.000101 |
| MALLORCA-BALEARES- | 1 | 0.000101 |
| NO RESIDENTE | 1 | 0.000101 |
| TERRUEL | 1 | 0.000101 |
| TERRAGONA | 1 | 0.000101 |
| Illes Ballears | 1 | 0.000101 |
| Vallodolid | 1 | 0.000101 |
| Peñiscola | 1 | 0.000101 |
| MALGA | 1 | 0.000101 |
| guipuzkoa | 1 | 0.000101 |
| GUIPOUZCOA | 1 | 0.000101 |
| palencia | 1 | 0.000101 |
| PONTEVEDRA. SALVATERRA DE MIÑO | 1 | 0.000101 |
| BIZKAIYA | 1 | 0.000101 |
| Pontevdra | 1 | 0.000101 |
| PONTEVDRA | 1 | 0.000101 |
| Mállaga | 1 | 0.000101 |
| Cundinamarca | 1 | 0.000101 |
| Addis Ababa | 1 | 0.000101 |
| NAVARA | 1 | 0.000101 |
| SANTA CRUZ DETENERIFE | 1 | 0.000101 |
| OTUR VALDES (LUARCA) | 1 | 0.000101 |
| Gipizkoa | 1 | 0.000101 |
| LLIEDA | 1 | 0.000101 |
| GIPUSCUA | 1 | 0.000101 |
| A CORNUÑA | 1 | 0.000101 |
| santarder | 1 | 0.000101 |
| Illes De Balears | 1 | 0.000101 |
| Guadalaja | 1 | 0.000101 |
| SEVIILA | 1 | 0.000101 |
| balears | 1 | 0.000101 |
| Donosti | 1 | 0.000101 |
| Santiago | 1 | 0.000101 |
| ALBAZETE | 1 | 0.000101 |
| SANTA CRUZ DE TRENERIFE | 1 | 0.000101 |
| Extremadura | 1 | 0.000101 |
| VALENCIO | 1 | 0.000101 |
| TALABERA DE LA REINA | 1 | 0.000101 |
| LAS PALAMAS DE GRAN CANARIA | 1 | 0.000101 |
| GORLIZ | 1 | 0.000101 |
| ALICANTE / DILAJOYOSA | 1 | 0.000101 |
| HAMBURG | 1 | 0.000101 |
| Donostia | 1 | 0.000101 |
| LA CORU?A | 1 | 0.000101 |
| BARACALDO | 1 | 0.000101 |
| Getxo/Bizkaia | 1 | 0.000101 |
| Santa Cruz De La Palma | 1 | 0.000101 |
| g.c | 1 | 0.000101 |
| GELVES | 1 | 0.000101 |
| la coruña | 1 | 0.000101 |
| Águilas | 1 | 0.000101 |
| MARIA DE HUERVA | 1 | 0.000101 |
| Araba/Álaba | 1 | 0.000101 |
| PO | 1 | 0.000101 |
| DE JAEN | 1 | 0.000101 |
| OURENSE/ORENSE | 1 | 0.000101 |
| Ibiza | 1 | 0.000101 |
| Tarrronga | 1 | 0.000101 |
| Alicante (Alacant) | 1 | 0.000101 |
| VIZCAYIA | 1 | 0.000101 |
| Barcleona | 1 | 0.000101 |
| Antwerp | 1 | 0.000101 |
| Guipuscoa | 1 | 0.000101 |
| Valdegovía | 1 | 0.000101 |
| Guadajalara | 1 | 0.000101 |
| IRUN | 1 | 0.000101 |
| CARTEGENA | 1 | 0.000101 |
| CASTELLoN | 1 | 0.000101 |
| ciudda real | 1 | 0.000101 |
| PONTEVEDRO | 1 | 0.000101 |
| LAS PALMAS LANZAROTE | 1 | 0.000101 |
| Pontebra | 1 | 0.000101 |
| Bizkaya | 1 | 0.000101 |
| Las Palamas | 1 | 0.000101 |
| Alicanta | 1 | 0.000101 |
| Cantábria | 1 | 0.000101 |
| Paraná | 1 | 0.000101 |
| CARRERA DEL CARMEN 22 | 1 | 0.000101 |
| Pontevendra | 1 | 0.000101 |
| Taragona | 1 | 0.000101 |
| teruel | 1 | 0.000101 |
| VALLADOLD | 1 | 0.000101 |
| CASTILLA | 1 | 0.000101 |
| Gipuzkia | 1 | 0.000101 |
| PALMAS DE GRAN CANARIAS | 1 | 0.000101 |
| guipuzcoa | 1 | 0.000101 |
| NULL | 1 | 0.000101 |
| Arava/Álava | 1 | 0.000101 |
| AvilA | 1 | 0.000101 |
| SANTIAGO COMPOSTELA | 1 | 0.000101 |
| gijón | 1 | 0.000101 |
| CORUÑA A | 1 | 0.000101 |
| Araba/álava | 1 | 0.000101 |
| BÉLGICA | 1 | 0.000101 |
| Gipuzloa | 1 | 0.000101 |
| palma de mallorca | 1 | 0.000101 |
| Gipuzkua | 1 | 0.000101 |
| Garnada | 1 | 0.000101 |
| Las Palma | 1 | 0.000101 |
| vigo | 1 | 0.000101 |
| BARCO DE AVILA (AVILA) | 1 | 0.000101 |
| a Coruña | 1 | 0.000101 |
| rARRAGONA | 1 | 0.000101 |
| Zarago | 1 | 0.000101 |
| Guipozcoa | 1 | 0.000101 |
| New York | 1 | 0.000101 |
| ILLES | 1 | 0.000101 |
| CORU?A,A | 1 | 0.000101 |
| araba | 1 | 0.000101 |
| Santa Cruz De Tenerfie | 1 | 0.000101 |
| ARONA | 1 | 0.000101 |
| BIZKAYA | 1 | 0.000101 |
| Tarrragona | 1 | 0.000101 |
| A CORUA | 1 | 0.000101 |
| AUSTURIAS | 1 | 0.000101 |
| MALLORCA -BALEARES | 1 | 0.000101 |
| Loire-Atlantique | 1 | 0.000101 |
| Montcada I Reixac | 1 | 0.000101 |
| CORBOBA | 1 | 0.000101 |
| Illes Belears | 1 | 0.000101 |
| VIZAYA | 1 | 0.000101 |
| Santa Cruz De Tenerife Canarias | 1 | 0.000101 |
| albacete | 1 | 0.000101 |
| BURGS | 1 | 0.000101 |
| Santa Cruz sde Tenerife | 1 | 0.000101 |
| Todelo | 1 | 0.000101 |
| GUIPúZCOA | 1 | 0.000101 |
| IILES BALEARS | 1 | 0.000101 |
| SANTA CRUZ DE TENERIFE DE TENERIFE | 1 | 0.000101 |
| PEILAGOS | 1 | 0.000101 |
| PICAXEN | 1 | 0.000101 |
| PONTVEDRRDA | 1 | 0.000101 |
| MIERES | 1 | 0.000101 |
| EL HIERRO | 1 | 0.000101 |
| TELDE | 1 | 0.000101 |
| BURJASOL | 1 | 0.000101 |
| PLASENCIA | 1 | 0.000101 |
| ZARAGONA | 1 | 0.000101 |
| BAEARES | 1 | 0.000101 |
| SANT CRUZ DE TENERIFE | 1 | 0.000101 |
| BIZAKAIA | 1 | 0.000101 |
| BENALMADENA | 1 | 0.000101 |
| GUIPUCOA | 1 | 0.000101 |
| ARABA ALAVA | 1 | 0.000101 |
| ARAGON | 1 | 0.000101 |
| CASTELLÓN DE LA PLANA | 1 | 0.000101 |
| ALGUAZAS | 1 | 0.000101 |
| JAéN | 1 | 0.000101 |
| CORRALEJOS | 1 | 0.000101 |
| SUECA | 1 | 0.000101 |
| VICTORIA | 1 | 0.000101 |
| SAN SESBAST | 1 | 0.000101 |
| LA LAGUNA TENERIFE | 1 | 0.000101 |
| GRANJA | 1 | 0.000101 |
| Bsarcelona | 1 | 0.000101 |
| Vizacaya | 1 | 0.000101 |
| ELCHE | 1 | 0.000101 |
| guipzkoa | 1 | 0.000101 |
| FUERTE VENTURA | 1 | 0.000101 |
| BADALONA | 1 | 0.000101 |
| J | 1 | 0.000101 |
| APOLA | 1 | 0.000101 |
| PALMA DE GRAN CANARIA | 1 | 0.000101 |
| S/C DE TENERIFE | 1 | 0.000101 |
| TARRAGON | 1 | 0.000101 |
| LEOn | 1 | 0.000101 |
| Gudalajara | 1 | 0.000101 |
| SANTA CRUZ DE TENERIFA | 1 | 0.000101 |
| Castello | 1 | 0.000101 |
| BALERAES | 1 | 0.000101 |
| EL VERGEL | 1 | 0.000101 |
| Skåne | 1 | 0.000101 |
| Castellón/Castello | 1 | 0.000101 |
| Guipuzcuoa | 1 | 0.000101 |
| GUIPOZ | 1 | 0.000101 |
| LA RIJOA | 1 | 0.000101 |
| GUPUZCOA | 1 | 0.000101 |
| ANDALUCÍA | 1 | 0.000101 |
| Ãlava | 1 | 0.000101 |
| ALMERÃA | 1 | 0.000101 |
| ALBECETE | 1 | 0.000101 |
| BIzkaia | 1 | 0.000101 |
| Francia | 1 | 0.000101 |
| MurciA | 1 | 0.000101 |
| VIZCAA | 1 | 0.000101 |
| Castellón/Castelló | 1 | 0.000101 |
| AlmerÃa | 1 | 0.000101 |
| CASTELLO N | 1 | 0.000101 |
| TARAGONA | 1 | 0.000101 |
| PALMAS,LAS | 1 | 0.000101 |
| VIZACAYA | 1 | 0.000101 |
| UTRERA | 1 | 0.000101 |
| VIZKAIA | 1 | 0.000101 |
| PONTEVENDRA | 1 | 0.000101 |
| NERIDA | 1 | 0.000101 |
| LA PALMAS | 1 | 0.000101 |
| STA DE CRUZ DE TENERIFE | 1 | 0.000101 |
| CantabrIa | 1 | 0.000101 |
| a coruñpa | 1 | 0.000101 |
| VALLADOLIS | 1 | 0.000101 |
| VALLADALID | 1 | 0.000101 |
| VALENIA | 1 | 0.000101 |
| GUIPOCUA | 1 | 0.000101 |
| EL HIERRO SANTA CRUZ DE TENERIFE | 1 | 0.000101 |
| EL HIERRO CANARIAS | 1 | 0.000101 |
| Coto de Bornos | 1 | 0.000101 |
| PASCO VASCO | 1 | 0.000101 |
| Mayorca | 1 | 0.000101 |
| EVILLA | 1 | 0.000101 |
| Cádiaz | 1 | 0.000101 |
| DENIA | 1 | 0.000101 |
| VITORIA GASTEIZ | 1 | 0.000101 |
| STA LUCIA TIRAJANAGRAN CANARIA | 1 | 0.000101 |
| TENERIFE CANARIAS | 1 | 0.000101 |
| VIZCVAYA | 1 | 0.000101 |
| gRANADA | 1 | 0.000101 |
| GRANDA | 1 | 0.000101 |
| FRONSAC | 1 | 0.000101 |
| MOTRIL | 1 | 0.000101 |
| CAMAS | 1 | 0.000101 |
| guipuzcua | 1 | 0.000101 |
| MUERCIA | 1 | 0.000101 |
| POLA DE LENA-ASTURIAS | 1 | 0.000101 |
| CATALUÑA | 1 | 0.000101 |
| AlIcante | 1 | 0.000101 |
| VALLODOLID | 1 | 0.000101 |
| avenida de francia 60,c portal 6 3º d | 1 | 0.000101 |
| SEVILLLA | 1 | 0.000101 |
| LA LAGUNA STA CRUZ DE TENERIFE | 1 | 0.000101 |
| ZARAUTZ | 1 | 0.000101 |
| CASTILLA-LA MANCHA | 1 | 0.000101 |
| GRANADAS | 1 | 0.000101 |
| EL FRANCO ASTURIAS | 1 | 0.000101 |
| SANTA CURZ DE TENERIFE | 1 | 0.000101 |
| CASTILLA DE LEON | 1 | 0.000101 |
| GIPUZCUA | 1 | 0.000101 |
| GUALAJARA | 1 | 0.000101 |
| GUIPUZOCA | 1 | 0.000101 |
| GuIpUzcoa | 1 | 0.000101 |
| ANDALUCIA | 1 | 0.000101 |
| LA ALBERCA | 1 | 0.000101 |
| Bizckai | 1 | 0.000101 |
| MAGALA | 1 | 0.000101 |
| Alicate | 1 | 0.000101 |
| Turias | 1 | 0.000101 |
| Vizvaya | 1 | 0.000101 |
| Arona | 1 | 0.000101 |
| Murica | 1 | 0.000101 |
| Lerida | 1 | 0.000101 |
| Viscaya | 1 | 0.000101 |
| Valldolid | 1 | 0.000101 |
| Vlencia | 1 | 0.000101 |
| Andorra | 1 | 0.000101 |
| Andalucia | 1 | 0.000101 |
| MALPICA DE BERGANTIÑOS | 1 | 0.000101 |
| badajod | 1 | 0.000101 |
| Chiang Mai | 1 | 0.000101 |
| HUEVA | 1 | 0.000101 |
| Palma | 1 | 0.000101 |
| GUIPUZCOU | 1 | 0.000101 |
| NAVARRO | 1 | 0.000101 |
| BizKaia | 1 | 0.000101 |
| Fontanarejo | 1 | 0.000101 |
| Mälaga | 1 | 0.000101 |
| SANTA EULALIA DEL RIO | 1 | 0.000101 |
| ZAROGAZA | 1 | 0.000101 |
| Matarrubia | 1 | 0.000101 |
| soria | 1 | 0.000101 |
| Buenos Aires | 1 | 0.000101 |
| Wakiso District | 1 | 0.000101 |
| Las Palmas Telde | 1 | 0.000101 |
| ILES BALEARS | 1 | 0.000101 |
| FUERTEVENTURA | 1 | 0.000101 |
| Valéncia | 1 | 0.000101 |
| CANTAMBRIA | 1 | 0.000101 |
| Valencia/Val?ncia | 1 | 0.000101 |
| SANTA CRUZ DE TENERFIE | 1 | 0.000101 |
| ISLAS BALERES | 1 | 0.000101 |
| GUIPUIZCOA | 1 | 0.000101 |
| LA CARUÑA | 1 | 0.000101 |
| LAS PALMAS (LANZAROTE) | 1 | 0.000101 |
| PATERNA | 1 | 0.000101 |
| VALLADOLIDAD | 1 | 0.000101 |
| GUIPOZKOA | 1 | 0.000101 |
| FELANITX | 1 | 0.000101 |
| LleIda | 1 | 0.000101 |
| La Coru?a | 1 | 0.000101 |
| SAN SE BASTIAN | 1 | 0.000101 |
| PONTEVERA | 1 | 0.000101 |
| CALLELLON | 1 | 0.000101 |
| CIUDAR REAL | 1 | 0.000101 |
| BRION | 1 | 0.000101 |
| baleares | 1 | 0.000101 |
| CUDAD REAL | 1 | 0.000101 |
| STA CRUZ DE TERENIFE | 1 | 0.000101 |
| LERIDA/LLEIDA | 1 | 0.000101 |
| BEJAR | 1 | 0.000101 |
| GUIPOZCOA | 1 | 0.000101 |
| La Pama | 1 | 0.000101 |
| PONFERRADA | 1 | 0.000101 |
| Astudias | 1 | 0.000101 |
| MelillA | 1 | 0.000101 |
| Hertfordshire | 1 | 0.000101 |
| Munchen | 1 | 0.000101 |
| SANTA CRUZ DE La PALMA | 1 | 0.000101 |
| Sta Cruz De Tenerife | 1 | 0.000101 |
| lanzarote | 1 | 0.000101 |
| Tarrgona | 1 | 0.000101 |
| SUIZA | 1 | 0.000101 |
| La rioja | 1 | 0.000101 |
| Bogotà | 1 | 0.000101 |
| Virginia | 1 | 0.000101 |
| ILLES BALEARS MENORCA | 1 | 0.000101 |
| Castilla y León | 1 | 0.000101 |
| ALMERiA | 1 | 0.000101 |
| GUIPIZCOA | 1 | 0.000101 |
| Niedersachsen | 1 | 0.000101 |
| BARCELONAc23090 | 1 | 0.000101 |
| Alicane | 1 | 0.000101 |
| Alicante/Alcant | 1 | 0.000101 |
| VILLANUEVA DE AROSA | 1 | 0.000101 |
| AQUITANIA | 1 | 0.000101 |
| Castellóna | 1 | 0.000101 |
| PORTUGAL | 1 | 0.000101 |
| FERROL | 1 | 0.000101 |
| Toledo. | 1 | 0.000101 |
| CASTELLO | 1 | 0.000101 |
| Valrencia | 1 | 0.000101 |
| Terrassa | 1 | 0.000101 |
| Hauts de Seine | 1 | 0.000101 |
| Fuengirola | 1 | 0.000101 |
| MÉRIDA | 1 | 0.000101 |
| S.C. TENERIFE | 1 | 0.000101 |
| Islas Balears | 1 | 0.000101 |
| PAMPLOANA | 1 | 0.000101 |
| GuipuzcoA | 1 | 0.000101 |
| BAdAjoz | 1 | 0.000101 |
| LAs PAlmAs | 1 | 0.000101 |
| A Coruna | 1 | 0.000101 |
| sant sadurni de noia | 1 | 0.000101 |
| San Sebastin | 1 | 0.000101 |
| España | 1 | 0.000101 |
| AlbAcete | 1 | 0.000101 |
| Albacte | 1 | 0.000101 |
| Cieza | 1 | 0.000101 |
| SALALANCA | 1 | 0.000101 |
| ALVA | 1 | 0.000101 |
| Castellón de la Plana | 1 | 0.000101 |
| VALENCA | 1 | 0.000101 |
| CAstellOn/CAstellO | 1 | 0.000101 |
| Guipúscoa | 1 | 0.000101 |
| Pontvendra | 1 | 0.000101 |
| Santa Cruz de Tenerifie | 1 | 0.000101 |
| Rioja,la | 1 | 0.000101 |
| Vizcaia | 1 | 0.000101 |
| XATIVA | 1 | 0.000101 |
| Luego | 1 | 0.000101 |
| Badiajoz | 1 | 0.000101 |
| Algeciras | 1 | 0.000101 |
| TARRAGORRA | 1 | 0.000101 |
| YEIDA | 1 | 0.000101 |
| Barcelna | 1 | 0.000101 |
| Las Baleares | 1 | 0.000101 |
| Guipuzkuo | 1 | 0.000101 |
| Gupuzcoa | 1 | 0.000101 |
| ARRASATE/MONDRAGON | 1 | 0.000101 |
| Sta.cruz Tenerife | 1 | 0.000101 |
| Barelona | 1 | 0.000101 |
| VAlencia | 1 | 0.000101 |
| Catarroja | 1 | 0.000101 |
| Castellon/Castelló | 1 | 0.000101 |
| Albate | 1 | 0.000101 |
| Navara | 1 | 0.000101 |
| Las Palmas - Telde | 1 | 0.000101 |
| LAS PALMAS DE GRAN CANARIOS | 1 | 0.000101 |
| gerona | 1 | 0.000101 |
| Schwieberdingen | 1 | 0.000101 |
| Iiles Balears | 1 | 0.000101 |
| Aragón | 1 | 0.000101 |
| Alicante. | 1 | 0.000101 |
| TARRAGAONA | 1 | 0.000101 |
| A | 1 | 0.000101 |
| CALVIA | 1 | 0.000101 |
| PALMA DE MALORCA | 1 | 0.000101 |
| LAS PALMAS GRAN CANARIAS | 1 | 0.000101 |
| LANZARATE | 1 | 0.000101 |
| islas Baleares | 1 | 0.000101 |
| ON | 1 | 0.000101 |
| Castilla y la Mancha | 1 | 0.000101 |
| Sant vicente | 1 | 0.000101 |
| Cordoba Ibarruri 3 esc 1 3 1 | 1 | 0.000101 |
| Corboda | 1 | 0.000101 |
| STOCKHOLM | 1 | 0.000101 |
| PARIS | 1 | 0.000101 |
| Paris | 1 | 0.000101 |
| VICTORAI GAXTEIZ | 1 | 0.000101 |
| BAYONA | 1 | 0.000101 |
| LE0N | 1 | 0.000101 |
| LAS PALMAS GRAN CANARIA | 1 | 0.000101 |
| bizcaia | 1 | 0.000101 |
| VILLAPEDRE | 1 | 0.000101 |
| VAL DE MARNE | 1 | 0.000101 |
| BABIERA | 1 | 0.000101 |
| SANTA CRUZ DE LA PALMA | 1 | 0.000101 |
| Arava | 1 | 0.000101 |
| GRAN CANARIAS - LAS PALMAS | 1 | 0.000101 |
| A CORUNA | 1 | 0.000101 |
| KINSHASA | 1 | 0.000101 |
| Centro | 1 | 0.000101 |
| ORENZE | 1 | 0.000101 |
| Lagunes | 1 | 0.000101 |
| Leste | 1 | 0.000101 |
| Sud -Kivu | 1 | 0.000101 |
| NORD KIVU | 1 | 0.000101 |
| Zinder | 1 | 0.000101 |
| Madrdi | 1 | 0.000101 |
| Elfashir | 1 | 0.000101 |
| gipuzkoa | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "npsp__largest_soft_credit_amount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npsp__largest_soft_credit_amount__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npsp__largest_soft_credit_amount__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_last_year__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_last_year__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_last_year__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_this_year__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_this_year__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_this_year__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "npo02__soft_credit_two_years_ago__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__soft_credit_two_years_ago__c es 994064. Lo que supone un 100.0% El nº de vacios para la variable npo02__soft_credit_two_years_ago__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot |
|---|
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondoscp__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondoscp__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 820400 | 82.529897 |
| True | 173664 | 17.470103 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosemail__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondosemail__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 852927 | 85.802021 |
| True | 141137 | 14.197979 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondosmi__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondosmi__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 886401 | 89.16941 |
| True | 107663 | 10.83059 |
# Vamos a realizar analisis por cada variable
var = "msf_nocaptacionfondossms__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_nocaptacionfondossms__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 884392 | 88.96731 |
| True | 109672 | 11.03269 |
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaignentryrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaignentryrecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaignentryrecurringdonor__c es 589. Lo que supone un 0.05925171819923063%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mr4CQAQ | 37787 | 3.801264 |
| 7013Y000001mr2DQAQ | 31300 | 3.148691 |
| 7013Y000001mr2cQAA | 26419 | 2.657676 |
| 7013Y000001mrCzQAI | 25969 | 2.612407 |
| 7013Y000001mrBSQAY | 24008 | 2.415136 |
| ... | ... | ... |
| 7013Y000001mrOuQAI | 1 | 0.000101 |
| 7013Y000001mrGjQAI | 1 | 0.000101 |
| 7013Y000001mrUjQAI | 1 | 0.000101 |
| 7013Y000001mqxTQAQ | 1 | 0.000101 |
| 7013Y000001mre3QAA | 1 | 0.000101 |
2565 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_firstcampaingcolaboration__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstcampaingcolaboration__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_firstcampaingcolaboration__c es 45219. Lo que supone un 4.548902283957572%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 45219 | 4.548902 | |
| 7013Y000001mrCzQAI | 38402 | 3.863132 |
| 7013Y000001mr4CQAQ | 34914 | 3.512249 |
| 7013Y000001mr2DQAQ | 27073 | 2.723466 |
| 7013Y000001mr2cQAA | 23877 | 2.401958 |
| ... | ... | ... |
| 7013Y000001mqs2QAA | 1 | 0.000101 |
| 7013Y000001mrO2QAI | 1 | 0.000101 |
| 7013Y000001mquxQAA | 1 | 0.000101 |
| 7013Y000001mqzYQAQ | 1 | 0.000101 |
| 7013Y000001mqyeQAA | 1 | 0.000101 |
2742 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_firstannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_firstannualizedquota__c es 32507. Lo que supone un 3.270111381158557% El nº de vacios para la variable msf_firstannualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 287648 | 29.914815 |
| 6.000000e+01 | 123651 | 12.859456 |
| 1.800000e+02 | 103083 | 10.720425 |
| 2.400000e+02 | 51256 | 5.330521 |
| 7.200000e+01 | 49342 | 5.131469 |
| 1.440000e+02 | 42521 | 4.422099 |
| 7.212000e+01 | 32909 | 3.422470 |
| 3.600000e+01 | 25308 | 2.631981 |
| 3.600000e+02 | 18085 | 1.880804 |
| 9.600000e+01 | 16441 | 1.709831 |
| 3.000000e+02 | 14049 | 1.461068 |
| 1.000000e+02 | 13112 | 1.363622 |
| 5.000000e+01 | 11678 | 1.214489 |
| 5.196000e+01 | 11000 | 1.143978 |
| 0.000000e+00 | 9343 | 0.971653 |
| 4.000000e+01 | 8834 | 0.918718 |
| 6.010000e+01 | 8598 | 0.894175 |
| 8.400000e+01 | 7852 | 0.816592 |
| 3.005000e+01 | 7602 | 0.790593 |
| 2.000000e+01 | 7382 | 0.767713 |
| 8.000000e+01 | 7025 | 0.730586 |
| 3.000000e+01 | 6974 | 0.725282 |
| 1.202000e+02 | 6514 | 0.677443 |
| 1.442400e+02 | 5434 | 0.565125 |
| 4.800000e+01 | 5204 | 0.541206 |
| 2.163600e+02 | 4876 | 0.507094 |
| 2.000000e+02 | 4820 | 0.501270 |
| 6.000000e+02 | 4229 | 0.439808 |
| 3.606000e+02 | 3825 | 0.397792 |
| 1.200000e+01 | 3772 | 0.392280 |
| 1.000000e+01 | 3465 | 0.360353 |
| 1.500000e+02 | 3268 | 0.339865 |
| 1.803000e+01 | 3166 | 0.329258 |
| 1.320000e+02 | 2999 | 0.311890 |
| 2.160000e+02 | 2293 | 0.238467 |
| 1.500000e+01 | 2293 | 0.238467 |
| 7.200000e+02 | 1971 | 0.204980 |
| 9.015000e+01 | 1774 | 0.184492 |
| 2.404000e+02 | 1725 | 0.179397 |
| 2.500000e+01 | 1672 | 0.173885 |
| 1.080000e+02 | 1569 | 0.163173 |
| 9.000000e+01 | 1565 | 0.162757 |
| 4.800000e+02 | 1354 | 0.140813 |
| 4.808000e+01 | 1208 | 0.125630 |
| 2.400000e+01 | 1120 | 0.116478 |
| 1.200000e+03 | 1049 | 0.109094 |
| 2.404000e+01 | 1043 | 0.108470 |
| 3.486000e+01 | 1041 | 0.108262 |
| 1.600000e+02 | 939 | 0.097654 |
| 1.560000e+02 | 856 | 0.089022 |
| 2.040000e+02 | 850 | 0.088398 |
| 4.000000e+02 | 814 | 0.084654 |
| 1.502500e+02 | 781 | 0.081222 |
| 7.212000e+02 | 779 | 0.081014 |
| 1.394400e+02 | 744 | 0.077375 |
| 3.606000e+01 | 724 | 0.075295 |
| 3.612000e+01 | 713 | 0.074151 |
| 1.082400e+02 | 652 | 0.067807 |
| 1.920000e+02 | 627 | 0.065207 |
| 1.040400e+02 | 598 | 0.062191 |
| 7.000000e+01 | 534 | 0.055535 |
| 1.803600e+02 | 503 | 0.052311 |
| 7.500000e+01 | 439 | 0.045655 |
| 6.010000e+00 | 382 | 0.039727 |
| 2.500000e+02 | 377 | 0.039207 |
| 1.730400e+02 | 376 | 0.039103 |
| 1.680000e+02 | 376 | 0.039103 |
| 4.200000e+02 | 353 | 0.036711 |
| 9.316000e+01 | 344 | 0.035775 |
| 1.202000e+01 | 341 | 0.035463 |
| 5.000000e+02 | 306 | 0.031823 |
| 2.884800e+02 | 304 | 0.031615 |
| 1.039200e+02 | 287 | 0.029847 |
| 5.000000e+00 | 273 | 0.028391 |
| 2.520000e+02 | 270 | 0.028079 |
| 9.616000e+01 | 262 | 0.027247 |
| 3.608000e+01 | 260 | 0.027039 |
| 7.224000e+01 | 254 | 0.026415 |
| 2.640000e+02 | 248 | 0.025792 |
| 1.803000e+02 | 248 | 0.025792 |
| 5.768000e+01 | 243 | 0.025272 |
| 2.880000e+02 | 243 | 0.025272 |
| 1.400000e+02 | 240 | 0.024960 |
| 5.200000e+01 | 229 | 0.023816 |
| 3.005100e+02 | 204 | 0.021216 |
| 4.183200e+02 | 204 | 0.021216 |
| 1.800000e+01 | 193 | 0.020072 |
| 1.000000e+03 | 177 | 0.018408 |
| 3.000000e+00 | 176 | 0.018304 |
| 6.000000e+00 | 146 | 0.015184 |
| 3.500000e+01 | 141 | 0.014664 |
| 6.012000e+01 | 141 | 0.014664 |
| 5.400000e+02 | 140 | 0.014560 |
| 2.885000e+01 | 138 | 0.014352 |
| 1.800000e+03 | 138 | 0.014352 |
| 4.207000e+01 | 136 | 0.014144 |
| 3.200000e+01 | 133 | 0.013832 |
| 4.320000e+02 | 116 | 0.012064 |
| 1.154000e+02 | 114 | 0.011856 |
| 1.250000e+02 | 112 | 0.011648 |
| 3.462000e+02 | 108 | 0.011232 |
| 1.442000e+01 | 101 | 0.010504 |
| 8.414000e+01 | 100 | 0.010400 |
| 1.080000e+03 | 99 | 0.010296 |
| 4.500000e+01 | 95 | 0.009880 |
| 1.923200e+02 | 89 | 0.009256 |
| 1.081800e+03 | 86 | 0.008944 |
| 5.770000e+01 | 86 | 0.008944 |
| 5.409000e+01 | 80 | 0.008320 |
| 2.400000e+03 | 80 | 0.008320 |
| 4.327200e+02 | 77 | 0.008008 |
| 8.000000e+02 | 75 | 0.007800 |
| 4.200000e+01 | 75 | 0.007800 |
| 6.010000e+02 | 71 | 0.007384 |
| 9.600000e+02 | 70 | 0.007280 |
| 6.010100e+02 | 68 | 0.007072 |
| 1.300000e+02 | 66 | 0.006864 |
| 9.000000e+02 | 64 | 0.006656 |
| 8.400000e+02 | 62 | 0.006448 |
| 3.960000e+02 | 61 | 0.006344 |
| 4.808000e+02 | 61 | 0.006344 |
| 2.760000e+02 | 60 | 0.006240 |
| 1.440000e+03 | 60 | 0.006240 |
| 8.000000e+00 | 60 | 0.006240 |
| 3.120000e+02 | 59 | 0.006136 |
| 1.500000e+03 | 55 | 0.005720 |
| 5.500000e+01 | 54 | 0.005616 |
| 1.081800e+02 | 53 | 0.005512 |
| 3.726400e+02 | 52 | 0.005408 |
| 5.769600e+02 | 48 | 0.004992 |
| 1.100000e+02 | 47 | 0.004888 |
| 1.204000e+01 | 46 | 0.004784 |
| 3.600000e+03 | 46 | 0.004784 |
| 1.803200e+02 | 45 | 0.004680 |
| 1.682800e+02 | 45 | 0.004680 |
| 2.404100e+02 | 43 | 0.004472 |
| 3.614400e+02 | 41 | 0.004264 |
| 3.240000e+02 | 41 | 0.004264 |
| 6.500000e+01 | 40 | 0.004160 |
| 3.200000e+02 | 39 | 0.004056 |
| 5.048400e+02 | 39 | 0.004056 |
| 1.442400e+03 | 39 | 0.004056 |
| 2.524800e+02 | 39 | 0.004056 |
| 5.400000e+01 | 38 | 0.003952 |
| 2.280000e+02 | 38 | 0.003952 |
| 3.840000e+02 | 37 | 0.003848 |
| 1.600000e+01 | 36 | 0.003744 |
| 8.654400e+02 | 36 | 0.003744 |
| 2.800000e+02 | 36 | 0.003744 |
| 1.082000e+02 | 36 | 0.003744 |
| 8.460000e+01 | 36 | 0.003744 |
| 2.000000e+03 | 33 | 0.003432 |
| 9.020000e+00 | 33 | 0.003432 |
| 2.200000e+02 | 32 | 0.003328 |
| 2.800000e+01 | 32 | 0.003328 |
| 3.650000e+02 | 30 | 0.003120 |
| 1.204800e+02 | 30 | 0.003120 |
| 3.360000e+02 | 30 | 0.003120 |
| 3.000000e+03 | 30 | 0.003120 |
| 3.500000e+02 | 29 | 0.003016 |
| 1.040000e+02 | 29 | 0.003016 |
| 1.094400e+02 | 27 | 0.002808 |
| 6.924000e+02 | 26 | 0.002704 |
| 6.000000e+03 | 26 | 0.002704 |
| 8.416000e+01 | 25 | 0.002600 |
| 3.720000e+02 | 25 | 0.002600 |
| 3.606100e+02 | 24 | 0.002496 |
| 7.813000e+01 | 24 | 0.002496 |
| 8.800000e+01 | 24 | 0.002496 |
| 5.040000e+02 | 23 | 0.002392 |
| 1.700000e+02 | 23 | 0.002392 |
| 2.600000e+02 | 23 | 0.002392 |
| 1.803000e+03 | 23 | 0.002392 |
| 6.024000e+01 | 22 | 0.002288 |
| 6.600000e+01 | 22 | 0.002288 |
| 5.600000e+01 | 22 | 0.002288 |
| 1.503000e+01 | 22 | 0.002288 |
| 3.900000e+01 | 21 | 0.002184 |
| 3.010000e+00 | 20 | 0.002080 |
| 4.680000e+02 | 20 | 0.002080 |
| 9.200000e+01 | 18 | 0.001872 |
| 2.160000e+01 | 18 | 0.001872 |
| 1.750000e+02 | 18 | 0.001872 |
| 3.800000e+01 | 18 | 0.001872 |
| 8.652000e+01 | 17 | 0.001768 |
| 1.824000e+02 | 17 | 0.001768 |
| 6.600000e+02 | 17 | 0.001768 |
| 8.500000e+01 | 16 | 0.001664 |
| 2.308000e+02 | 16 | 0.001664 |
| 2.103500e+02 | 16 | 0.001664 |
| 7.800000e+01 | 16 | 0.001664 |
| 4.400000e+01 | 16 | 0.001664 |
| 4.080000e+02 | 15 | 0.001560 |
| 1.520000e+02 | 15 | 0.001560 |
| 6.800000e+01 | 14 | 0.001456 |
| 8.640000e+02 | 14 | 0.001456 |
| 6.400000e+01 | 13 | 0.001352 |
| 3.012000e+01 | 13 | 0.001352 |
| 3.005000e+02 | 13 | 0.001352 |
| 1.200000e+04 | 13 | 0.001352 |
| 1.400000e+01 | 13 | 0.001352 |
| 6.120000e+02 | 13 | 0.001352 |
| 2.200000e+01 | 13 | 0.001352 |
| 1.201200e+02 | 12 | 0.001248 |
| 6.240000e+02 | 12 | 0.001248 |
| 7.000000e+00 | 12 | 0.001248 |
| 1.020000e+02 | 12 | 0.001248 |
| 4.000000e+00 | 12 | 0.001248 |
| 4.500000e+02 | 12 | 0.001248 |
| 1.719600e+02 | 12 | 0.001248 |
| 9.036000e+01 | 11 | 0.001144 |
| 3.606120e+03 | 11 | 0.001144 |
| 1.480000e+02 | 11 | 0.001144 |
| 5.760000e+02 | 11 | 0.001144 |
| 7.200000e+00 | 11 | 0.001144 |
| 1.202040e+03 | 11 | 0.001144 |
| 7.600000e+01 | 10 | 0.001040 |
| 4.332000e+01 | 10 | 0.001040 |
| 1.120000e+02 | 10 | 0.001040 |
| 7.920000e+02 | 10 | 0.001040 |
| 7.210000e+00 | 10 | 0.001040 |
| 9.012000e+01 | 9 | 0.000936 |
| 1.450000e+02 | 9 | 0.000936 |
| 4.508000e+01 | 9 | 0.000936 |
| 2.600000e+01 | 9 | 0.000936 |
| 1.280000e+02 | 9 | 0.000936 |
| 3.005200e+02 | 9 | 0.000936 |
| 3.480000e+02 | 9 | 0.000936 |
| 2.160000e+03 | 9 | 0.000936 |
| 9.000000e+00 | 8 | 0.000832 |
| 7.400000e+01 | 8 | 0.000832 |
| 5.289000e+01 | 8 | 0.000832 |
| 3.400000e+01 | 8 | 0.000832 |
| 7.228800e+02 | 8 | 0.000832 |
| 2.100000e+02 | 8 | 0.000832 |
| 7.800000e+02 | 7 | 0.000728 |
| 5.772000e+01 | 7 | 0.000728 |
| 6.200000e+01 | 7 | 0.000728 |
| 6.490800e+02 | 7 | 0.000728 |
| 7.300000e+01 | 7 | 0.000728 |
| 1.300000e+01 | 7 | 0.000728 |
| 5.300000e+01 | 7 | 0.000728 |
| 4.560000e+02 | 7 | 0.000728 |
| 3.365600e+02 | 7 | 0.000728 |
| 1.050000e+02 | 7 | 0.000728 |
| 6.611000e+01 | 7 | 0.000728 |
| 1.117920e+03 | 7 | 0.000728 |
| 1.350000e+02 | 7 | 0.000728 |
| 9.016000e+01 | 6 | 0.000624 |
| 1.160000e+02 | 6 | 0.000624 |
| 4.800000e+03 | 6 | 0.000624 |
| 1.020000e+03 | 6 | 0.000624 |
| 1.444000e+01 | 6 | 0.000624 |
| 7.932000e+01 | 6 | 0.000624 |
| 7.000000e+02 | 6 | 0.000624 |
| 5.052000e+01 | 6 | 0.000624 |
| 5.200000e+02 | 6 | 0.000624 |
| 2.250000e+02 | 6 | 0.000624 |
| 1.600000e+03 | 6 | 0.000624 |
| 2.163600e+03 | 6 | 0.000624 |
| 2.880000e+01 | 6 | 0.000624 |
| 7.212200e+02 | 6 | 0.000624 |
| 1.000000e+00 | 6 | 0.000624 |
| 3.700000e+01 | 5 | 0.000520 |
| 9.360000e+02 | 5 | 0.000520 |
| 9.996000e+01 | 5 | 0.000520 |
| 2.300000e+02 | 5 | 0.000520 |
| 1.100000e+01 | 5 | 0.000520 |
| 1.920000e+03 | 5 | 0.000520 |
| 1.240000e+02 | 5 | 0.000520 |
| 7.200000e-01 | 5 | 0.000520 |
| 1.320000e+03 | 5 | 0.000520 |
| 9.900000e+01 | 5 | 0.000520 |
| 5.409600e+02 | 5 | 0.000520 |
| 2.164000e+01 | 5 | 0.000520 |
| 1.440000e+01 | 5 | 0.000520 |
| 9.015200e+02 | 5 | 0.000520 |
| 7.200000e+03 | 5 | 0.000520 |
| 2.115600e+02 | 5 | 0.000520 |
| 4.400000e+02 | 4 | 0.000416 |
| 9.375600e+02 | 4 | 0.000416 |
| 1.560000e+03 | 4 | 0.000416 |
| 5.000000e+03 | 4 | 0.000416 |
| 1.360000e+02 | 4 | 0.000416 |
| 1.620000e+02 | 4 | 0.000416 |
| 9.496000e+01 | 4 | 0.000416 |
| 5.592000e+01 | 4 | 0.000416 |
| 4.507600e+02 | 4 | 0.000416 |
| 3.300000e+01 | 4 | 0.000416 |
| 1.700000e+01 | 4 | 0.000416 |
| 2.700000e+01 | 4 | 0.000416 |
| 5.412000e+01 | 4 | 0.000416 |
| 1.260000e+02 | 4 | 0.000416 |
| 6.400000e+02 | 4 | 0.000416 |
| 1.081200e+02 | 4 | 0.000416 |
| 1.400000e+03 | 4 | 0.000416 |
| 8.660000e+00 | 4 | 0.000416 |
| 1.250000e+01 | 4 | 0.000416 |
| 2.100000e+01 | 4 | 0.000416 |
| 2.404040e+03 | 4 | 0.000416 |
| 1.650000e+02 | 4 | 0.000416 |
| 2.409600e+02 | 4 | 0.000416 |
| 7.560000e+02 | 4 | 0.000416 |
| 1.502400e+02 | 4 | 0.000416 |
| 2.700000e+02 | 4 | 0.000416 |
| 2.300000e+01 | 4 | 0.000416 |
| 4.330000e+00 | 4 | 0.000416 |
| 4.000000e+03 | 3 | 0.000312 |
| 3.100000e+01 | 3 | 0.000312 |
| 4.207100e+02 | 3 | 0.000312 |
| 4.328000e+01 | 3 | 0.000312 |
| 1.081840e+03 | 3 | 0.000312 |
| 2.705000e+01 | 3 | 0.000312 |
| 9.616400e+02 | 3 | 0.000312 |
| 5.202000e+01 | 3 | 0.000312 |
| 7.500000e+02 | 3 | 0.000312 |
| 2.040000e+03 | 3 | 0.000312 |
| 1.226400e+02 | 3 | 0.000312 |
| 3.900000e+03 | 3 | 0.000312 |
| 2.104000e+01 | 3 | 0.000312 |
| 1.129900e+02 | 3 | 0.000312 |
| 7.513000e+01 | 3 | 0.000312 |
| 2.884920e+03 | 3 | 0.000312 |
| 1.009680e+03 | 3 | 0.000312 |
| 3.004000e+01 | 3 | 0.000312 |
| 3.330000e+02 | 3 | 0.000312 |
| 6.972000e+01 | 3 | 0.000312 |
| 3.996000e+01 | 3 | 0.000312 |
| 1.804000e+01 | 3 | 0.000312 |
| 1.803040e+03 | 3 | 0.000312 |
| 3.846400e+02 | 3 | 0.000312 |
| 2.884000e+01 | 3 | 0.000312 |
| 2.750000e+02 | 3 | 0.000312 |
| 4.440000e+02 | 3 | 0.000312 |
| 1.732000e+01 | 3 | 0.000312 |
| 1.212000e+03 | 3 | 0.000312 |
| 1.510000e+02 | 3 | 0.000312 |
| 3.125200e+02 | 3 | 0.000312 |
| 2.004000e+03 | 3 | 0.000312 |
| 3.400000e+02 | 3 | 0.000312 |
| 1.900000e+02 | 3 | 0.000312 |
| 6.360000e+02 | 3 | 0.000312 |
| 9.372000e+01 | 3 | 0.000312 |
| 5.100000e+01 | 3 | 0.000312 |
| 1.983300e+02 | 3 | 0.000312 |
| 6.480000e+02 | 3 | 0.000312 |
| 1.532600e+02 | 3 | 0.000312 |
| 1.983600e+02 | 3 | 0.000312 |
| 4.600000e+02 | 2 | 0.000208 |
| 1.800000e+04 | 2 | 0.000208 |
| 3.750000e+02 | 2 | 0.000208 |
| 5.988000e+01 | 2 | 0.000208 |
| 3.660000e+02 | 2 | 0.000208 |
| 1.280200e+02 | 2 | 0.000208 |
| 7.356000e+02 | 2 | 0.000208 |
| 3.666000e+01 | 2 | 0.000208 |
| 7.933200e+02 | 2 | 0.000208 |
| 2.884900e+02 | 2 | 0.000208 |
| 1.830000e+02 | 2 | 0.000208 |
| 1.850000e+02 | 2 | 0.000208 |
| 1.210000e+02 | 2 | 0.000208 |
| 4.800000e+00 | 2 | 0.000208 |
| 2.480000e+02 | 2 | 0.000208 |
| 4.600000e+01 | 2 | 0.000208 |
| 8.414000e+02 | 2 | 0.000208 |
| 1.322400e+02 | 2 | 0.000208 |
| 4.327000e+01 | 2 | 0.000208 |
| 1.200100e+02 | 2 | 0.000208 |
| 6.100000e+01 | 2 | 0.000208 |
| 2.644400e+02 | 2 | 0.000208 |
| 6.492000e+01 | 2 | 0.000208 |
| 1.640000e+02 | 2 | 0.000208 |
| 4.920000e+02 | 2 | 0.000208 |
| 5.500000e+02 | 2 | 0.000208 |
| 3.250000e+02 | 2 | 0.000208 |
| 2.520000e+01 | 2 | 0.000208 |
| 8.200000e+01 | 2 | 0.000208 |
| 1.502600e+02 | 2 | 0.000208 |
| 2.406000e+02 | 2 | 0.000208 |
| 7.440000e+01 | 2 | 0.000208 |
| 1.010000e+02 | 2 | 0.000208 |
| 6.500000e+02 | 2 | 0.000208 |
| 2.019600e+02 | 2 | 0.000208 |
| 2.403600e+02 | 2 | 0.000208 |
| 3.602400e+02 | 2 | 0.000208 |
| 4.200000e+03 | 2 | 0.000208 |
| 1.622400e+02 | 2 | 0.000208 |
| 8.700000e+01 | 2 | 0.000208 |
| 7.200000e+04 | 2 | 0.000208 |
| 3.006000e+01 | 2 | 0.000208 |
| 3.300000e+02 | 2 | 0.000208 |
| 1.802800e+02 | 2 | 0.000208 |
| 2.598000e+01 | 2 | 0.000208 |
| 9.999600e+02 | 2 | 0.000208 |
| 2.000000e+00 | 2 | 0.000208 |
| 7.700000e+01 | 2 | 0.000208 |
| 9.320000e+00 | 2 | 0.000208 |
| 2.900000e+01 | 2 | 0.000208 |
| 7.250000e+02 | 2 | 0.000208 |
| 1.202020e+03 | 2 | 0.000208 |
| 1.021700e+02 | 2 | 0.000208 |
| 4.808100e+02 | 2 | 0.000208 |
| 1.150000e+02 | 2 | 0.000208 |
| 9.500000e+01 | 2 | 0.000208 |
| 1.959600e+02 | 2 | 0.000208 |
| 5.520000e+02 | 2 | 0.000208 |
| 3.900000e+02 | 2 | 0.000208 |
| 1.008000e+03 | 2 | 0.000208 |
| 1.230000e+02 | 2 | 0.000208 |
| 1.586400e+02 | 2 | 0.000208 |
| 7.572000e+01 | 2 | 0.000208 |
| 1.382300e+02 | 2 | 0.000208 |
| 1.460000e+02 | 2 | 0.000208 |
| 7.212120e+03 | 2 | 0.000208 |
| 1.444800e+02 | 2 | 0.000208 |
| 4.300000e+01 | 2 | 0.000208 |
| 6.396000e+01 | 2 | 0.000208 |
| 4.519600e+02 | 2 | 0.000208 |
| 6.960000e+02 | 2 | 0.000208 |
| 1.740000e+02 | 2 | 0.000208 |
| 5.880000e+02 | 2 | 0.000208 |
| 8.656000e+01 | 2 | 0.000208 |
| 7.452000e+01 | 2 | 0.000208 |
| 4.320000e+03 | 2 | 0.000208 |
| 1.394000e+02 | 2 | 0.000208 |
| 7.320000e+01 | 2 | 0.000208 |
| 2.220000e+02 | 2 | 0.000208 |
| 8.040000e+02 | 2 | 0.000208 |
| 6.200000e+02 | 2 | 0.000208 |
| 7.320000e+02 | 2 | 0.000208 |
| 1.100000e+03 | 2 | 0.000208 |
| 7.440000e+02 | 2 | 0.000208 |
| 1.260000e+03 | 2 | 0.000208 |
| 3.607200e+02 | 2 | 0.000208 |
| 1.382000e+01 | 2 | 0.000208 |
| 3.060000e+02 | 2 | 0.000208 |
| 1.000100e+02 | 2 | 0.000208 |
| 1.200000e+00 | 2 | 0.000208 |
| 2.440000e+02 | 2 | 0.000208 |
| 1.355880e+03 | 2 | 0.000208 |
| 1.684000e+01 | 2 | 0.000208 |
| 1.960000e+02 | 2 | 0.000208 |
| 1.562800e+02 | 2 | 0.000208 |
| 9.100000e+01 | 2 | 0.000208 |
| 2.061500e+02 | 2 | 0.000208 |
| 5.408000e+01 | 2 | 0.000208 |
| 7.992000e+01 | 2 | 0.000208 |
| 1.250000e+03 | 2 | 0.000208 |
| 2.880000e+03 | 2 | 0.000208 |
| 5.196000e+02 | 2 | 0.000208 |
| 1.110000e+02 | 2 | 0.000208 |
| 6.130800e+02 | 2 | 0.000208 |
| 8.160000e+02 | 2 | 0.000208 |
| 1.154400e+02 | 2 | 0.000208 |
| 3.246000e+02 | 2 | 0.000208 |
| 2.379600e+02 | 2 | 0.000208 |
| 1.262100e+02 | 2 | 0.000208 |
| 2.550000e+02 | 1 | 0.000104 |
| 8.640000e+01 | 1 | 0.000104 |
| 9.204000e+01 | 1 | 0.000104 |
| 5.280000e+02 | 1 | 0.000104 |
| 9.999000e+01 | 1 | 0.000104 |
| 5.600000e+02 | 1 | 0.000104 |
| 4.692000e+01 | 1 | 0.000104 |
| 5.280000e+01 | 1 | 0.000104 |
| 6.840000e+02 | 1 | 0.000104 |
| 9.324000e+01 | 1 | 0.000104 |
| 5.160000e+01 | 1 | 0.000104 |
| 6.060000e+01 | 1 | 0.000104 |
| 2.240000e+02 | 1 | 0.000104 |
| 6.000000e-01 | 1 | 0.000104 |
| 6.015000e+01 | 1 | 0.000104 |
| 9.840000e+03 | 1 | 0.000104 |
| 2.476800e+02 | 1 | 0.000104 |
| 3.110000e+02 | 1 | 0.000104 |
| 6.600000e+03 | 1 | 0.000104 |
| 6.800000e+02 | 1 | 0.000104 |
| 2.100000e+03 | 1 | 0.000104 |
| 2.560000e+02 | 1 | 0.000104 |
| 1.716000e+02 | 1 | 0.000104 |
| 9.015100e+02 | 1 | 0.000104 |
| 3.010000e+02 | 1 | 0.000104 |
| 1.900000e+01 | 1 | 0.000104 |
| 2.150000e+02 | 1 | 0.000104 |
| 5.800000e+01 | 1 | 0.000104 |
| 1.202400e+02 | 1 | 0.000104 |
| 5.908000e+01 | 1 | 0.000104 |
| 1.680000e+03 | 1 | 0.000104 |
| 1.665600e+02 | 1 | 0.000104 |
| 1.250400e+02 | 1 | 0.000104 |
| 1.159200e+02 | 1 | 0.000104 |
| 6.235200e+02 | 1 | 0.000104 |
| 1.442440e+03 | 1 | 0.000104 |
| 2.720000e+02 | 1 | 0.000104 |
| 2.439600e+02 | 1 | 0.000104 |
| 3.800000e+02 | 1 | 0.000104 |
| 1.000800e+02 | 1 | 0.000104 |
| 5.040000e+01 | 1 | 0.000104 |
| 3.350000e+02 | 1 | 0.000104 |
| 2.253800e+03 | 1 | 0.000104 |
| 3.040000e+01 | 1 | 0.000104 |
| 1.052400e+02 | 1 | 0.000104 |
| 1.893600e+02 | 1 | 0.000104 |
| 1.446000e+02 | 1 | 0.000104 |
| 5.100000e+02 | 1 | 0.000104 |
| 1.296000e+03 | 1 | 0.000104 |
| 5.700000e+01 | 1 | 0.000104 |
| 2.560000e+01 | 1 | 0.000104 |
| 3.320000e+02 | 1 | 0.000104 |
| 1.812000e+02 | 1 | 0.000104 |
| 3.726000e+01 | 1 | 0.000104 |
| 2.960000e+02 | 1 | 0.000104 |
| 1.470000e+02 | 1 | 0.000104 |
| 1.860000e+03 | 1 | 0.000104 |
| 5.288000e+01 | 1 | 0.000104 |
| 1.140000e+03 | 1 | 0.000104 |
| 6.720000e+01 | 1 | 0.000104 |
| 6.876000e+01 | 1 | 0.000104 |
| 9.912000e+01 | 1 | 0.000104 |
| 1.658400e+02 | 1 | 0.000104 |
| 2.999000e+01 | 1 | 0.000104 |
| 1.238000e+02 | 1 | 0.000104 |
| 1.452000e+02 | 1 | 0.000104 |
| 1.208000e+02 | 1 | 0.000104 |
| 2.050000e+02 | 1 | 0.000104 |
| 2.000400e+02 | 1 | 0.000104 |
| 6.016000e+01 | 1 | 0.000104 |
| 4.208000e+01 | 1 | 0.000104 |
| 2.180000e+02 | 1 | 0.000104 |
| 4.100000e+01 | 1 | 0.000104 |
| 1.002000e+03 | 1 | 0.000104 |
| 7.812000e+01 | 1 | 0.000104 |
| 3.954800e+02 | 1 | 0.000104 |
| 3.005060e+04 | 1 | 0.000104 |
| 2.920000e+02 | 1 | 0.000104 |
| 1.472500e+02 | 1 | 0.000104 |
| 1.478520e+03 | 1 | 0.000104 |
| 6.346800e+02 | 1 | 0.000104 |
| 4.095600e+02 | 1 | 0.000104 |
| 2.496000e+03 | 1 | 0.000104 |
| 4.992000e+01 | 1 | 0.000104 |
| 6.001000e+01 | 1 | 0.000104 |
| 5.900000e+01 | 1 | 0.000104 |
| 1.586640e+03 | 1 | 0.000104 |
| 4.700000e+01 | 1 | 0.000104 |
| 1.056000e+02 | 1 | 0.000104 |
| 1.340000e+02 | 1 | 0.000104 |
| 8.246000e+02 | 1 | 0.000104 |
| 1.089600e+02 | 1 | 0.000104 |
| 1.947600e+02 | 1 | 0.000104 |
| 2.310000e+02 | 1 | 0.000104 |
| 6.660000e+01 | 1 | 0.000104 |
| 4.116000e+01 | 1 | 0.000104 |
| 1.300000e+03 | 1 | 0.000104 |
| 3.768000e+02 | 1 | 0.000104 |
| 2.340000e+02 | 1 | 0.000104 |
| 1.420000e+02 | 1 | 0.000104 |
| 2.388000e+02 | 1 | 0.000104 |
| 2.850000e+02 | 1 | 0.000104 |
| 3.780000e+02 | 1 | 0.000104 |
| 9.400000e+01 | 1 | 0.000104 |
| 1.036800e+02 | 1 | 0.000104 |
| 3.906600e+02 | 1 | 0.000104 |
| 4.928400e+02 | 1 | 0.000104 |
| 1.080000e+01 | 1 | 0.000104 |
| 5.048000e+01 | 1 | 0.000104 |
| 9.600000e+00 | 1 | 0.000104 |
| 1.380000e+03 | 1 | 0.000104 |
| 9.720000e+01 | 1 | 0.000104 |
| 9.096000e+01 | 1 | 0.000104 |
| 1.002000e+02 | 1 | 0.000104 |
| 1.870000e+02 | 1 | 0.000104 |
| 1.027200e+02 | 1 | 0.000104 |
| 9.232000e+01 | 1 | 0.000104 |
| 1.268400e+02 | 1 | 0.000104 |
| 3.885000e+01 | 1 | 0.000104 |
| 1.298400e+02 | 1 | 0.000104 |
| 5.160000e+02 | 1 | 0.000104 |
| 3.305600e+02 | 1 | 0.000104 |
| 3.336000e+02 | 1 | 0.000104 |
| 1.033760e+03 | 1 | 0.000104 |
| 2.043600e+02 | 1 | 0.000104 |
| 1.284000e+03 | 1 | 0.000104 |
| 5.944800e+02 | 1 | 0.000104 |
| 4.688400e+02 | 1 | 0.000104 |
| 1.800000e+00 | 1 | 0.000104 |
| 7.510000e+00 | 1 | 0.000104 |
| 6.010200e+02 | 1 | 0.000104 |
| 9.020000e+01 | 1 | 0.000104 |
| 3.065200e+02 | 1 | 0.000104 |
| 4.028000e+01 | 1 | 0.000104 |
| 8.292000e+01 | 1 | 0.000104 |
| 2.456676e+07 | 1 | 0.000104 |
| 2.596800e+02 | 1 | 0.000104 |
| 1.430400e+02 | 1 | 0.000104 |
| 2.200000e+03 | 1 | 0.000104 |
| 6.132000e+01 | 1 | 0.000104 |
| 1.322200e+02 | 1 | 0.000104 |
| 2.704600e+02 | 1 | 0.000104 |
| 7.220000e+01 | 1 | 0.000104 |
| 1.200000e+05 | 1 | 0.000104 |
| 1.840000e+02 | 1 | 0.000104 |
| 1.710000e+02 | 1 | 0.000104 |
| 1.502530e+03 | 1 | 0.000104 |
| 1.202000e+03 | 1 | 0.000104 |
| 4.580000e+02 | 1 | 0.000104 |
| 5.949600e+02 | 1 | 0.000104 |
| 2.307600e+02 | 1 | 0.000104 |
| 5.109000e+01 | 1 | 0.000104 |
| 3.124000e+01 | 1 | 0.000104 |
| 3.100000e+02 | 1 | 0.000104 |
| 6.010120e+03 | 1 | 0.000104 |
| 1.620000e+03 | 1 | 0.000104 |
| 2.524400e+02 | 1 | 0.000104 |
| 1.060000e+02 | 1 | 0.000104 |
| 6.720000e+02 | 1 | 0.000104 |
| 9.012000e+02 | 1 | 0.000104 |
| 2.500000e+03 | 1 | 0.000104 |
| 9.600000e+03 | 1 | 0.000104 |
| 4.182000e+02 | 1 | 0.000104 |
| 1.045760e+03 | 1 | 0.000104 |
| 9.840000e+02 | 1 | 0.000104 |
| 1.552000e+01 | 1 | 0.000104 |
| 2.046000e+02 | 1 | 0.000104 |
| 1.146720e+03 | 1 | 0.000104 |
| 1.030000e+02 | 1 | 0.000104 |
| 1.875600e+02 | 1 | 0.000104 |
| 9.720000e+02 | 1 | 0.000104 |
| 9.240000e+00 | 1 | 0.000104 |
| 3.612000e+03 | 1 | 0.000104 |
| 2.524300e+02 | 1 | 0.000104 |
| 2.402000e+01 | 1 | 0.000104 |
| 1.083600e+02 | 1 | 0.000104 |
| 1.092000e+02 | 1 | 0.000104 |
| 6.242400e+02 | 1 | 0.000104 |
| 5.870000e+00 | 1 | 0.000104 |
| 2.928000e+02 | 1 | 0.000104 |
| 1.983200e+02 | 1 | 0.000104 |
| 2.760000e+03 | 1 | 0.000104 |
| 8.166000e+03 | 1 | 0.000104 |
| 2.803600e+02 | 1 | 0.000104 |
| 1.980000e+02 | 1 | 0.000104 |
| 1.524000e+03 | 1 | 0.000104 |
| 1.203000e+01 | 1 | 0.000104 |
| 8.052648e+09 | 1 | 0.000104 |
| 1.080000e+06 | 1 | 0.000104 |
| 1.164000e+06 | 1 | 0.000104 |
| 7.596000e+01 | 1 | 0.000104 |
| 2.058000e+04 | 1 | 0.000104 |
| 1.253400e+02 | 1 | 0.000104 |
| 5.400000e+03 | 1 | 0.000104 |
| 1.239600e+02 | 1 | 0.000104 |
| 1.939200e+02 | 1 | 0.000104 |
| 9.015240e+03 | 1 | 0.000104 |
| 2.636000e+01 | 1 | 0.000104 |
| 7.230000e+01 | 1 | 0.000104 |
| 1.046400e+02 | 1 | 0.000104 |
| 6.008000e+01 | 1 | 0.000104 |
| 1.104000e+02 | 1 | 0.000104 |
| 4.059600e+02 | 1 | 0.000104 |
| 2.957040e+03 | 1 | 0.000104 |
| 7.220000e+00 | 1 | 0.000104 |
| 1.009200e+02 | 1 | 0.000104 |
| 4.900000e+01 | 1 | 0.000104 |
| 2.425000e+02 | 1 | 0.000104 |
| 4.484000e+01 | 1 | 0.000104 |
| 5.152800e+02 | 1 | 0.000104 |
| 8.925000e+01 | 1 | 0.000104 |
| 2.956800e+02 | 1 | 0.000104 |
| 2.410000e+02 | 1 | 0.000104 |
| 7.080000e+02 | 1 | 0.000104 |
| 2.064000e+03 | 1 | 0.000104 |
| 1.550000e+02 | 1 | 0.000104 |
| 1.834800e+02 | 1 | 0.000104 |
| 2.170800e+02 | 1 | 0.000104 |
| 2.401200e+02 | 1 | 0.000104 |
| 9.800000e+01 | 1 | 0.000104 |
| 3.889200e+02 | 1 | 0.000104 |
| 2.184000e+03 | 1 | 0.000104 |
| 4.700000e+02 | 1 | 0.000104 |
| 1.008000e+04 | 1 | 0.000104 |
| 1.536000e+03 | 1 | 0.000104 |
| 1.009600e+02 | 1 | 0.000104 |
| 5.460000e+01 | 1 | 0.000104 |
| 1.001500e+02 | 1 | 0.000104 |
| 1.678800e+02 | 1 | 0.000104 |
| 6.005000e+01 | 1 | 0.000104 |
| 1.812000e+03 | 1 | 0.000104 |
| 1.298160e+03 | 1 | 0.000104 |
| 1.490400e+02 | 1 | 0.000104 |
| 3.604000e+01 | 1 | 0.000104 |
| 3.996000e+02 | 1 | 0.000104 |
| 7.620000e+01 | 1 | 0.000104 |
| 3.700000e+02 | 1 | 0.000104 |
| 9.960000e+01 | 1 | 0.000104 |
| 8.016000e+01 | 1 | 0.000104 |
| 1.975200e+02 | 1 | 0.000104 |
| 6.300000e+01 | 1 | 0.000104 |
| 2.199720e+03 | 1 | 0.000104 |
| 1.056000e+03 | 1 | 0.000104 |
| 1.002800e+02 | 1 | 0.000104 |
| 1.090000e+02 | 1 | 0.000104 |
| 1.188000e+02 | 1 | 0.000104 |
| 6.006000e+01 | 1 | 0.000104 |
| 1.802400e+02 | 1 | 0.000104 |
| 1.500000e+04 | 1 | 0.000104 |
| 1.514400e+02 | 1 | 0.000104 |
| 6.020000e+02 | 1 | 0.000104 |
| 1.500100e+02 | 1 | 0.000104 |
| 3.000100e+02 | 1 | 0.000104 |
| 2.524200e+03 | 1 | 0.000104 |
| 2.193600e+02 | 1 | 0.000104 |
| 2.900000e+02 | 1 | 0.000104 |
| 1.501500e+02 | 1 | 0.000104 |
| 2.451600e+02 | 1 | 0.000104 |
| 5.493000e+01 | 1 | 0.000104 |
| 8.655000e+01 | 1 | 0.000104 |
| 1.211640e+03 | 1 | 0.000104 |
| 2.352000e+03 | 1 | 0.000104 |
| 2.196000e+02 | 1 | 0.000104 |
| 1.203600e+02 | 1 | 0.000104 |
| 3.607000e+01 | 1 | 0.000104 |
| 8.880000e+02 | 1 | 0.000104 |
| 1.200800e+02 | 1 | 0.000104 |
| 8.280000e+02 | 1 | 0.000104 |
| 7.204000e+01 | 1 | 0.000104 |
| 1.201200e+04 | 1 | 0.000104 |
| 8.900000e+01 | 1 | 0.000104 |
| 3.480000e+03 | 1 | 0.000104 |
| 3.230000e+02 | 1 | 0.000104 |
| 1.236000e+03 | 1 | 0.000104 |
| 4.119600e+02 | 1 | 0.000104 |
| 1.436400e+02 | 1 | 0.000104 |
| 1.340000e+01 | 1 | 0.000104 |
| 2.282400e+03 | 1 | 0.000104 |
| 5.950000e+01 | 1 | 0.000104 |
| 4.520000e+02 | 1 | 0.000104 |
| 6.480000e+01 | 1 | 0.000104 |
| 1.599600e+02 | 1 | 0.000104 |
| 1.220000e+02 | 1 | 0.000104 |
# Vamos a realizar analisis por cada variable
var = "msf_program__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_program__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_program__c es 27752. Lo que supone un 2.7917719583447345%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Reactivación bajas MASS | 473018 | 47.584260 |
| Cultivación socios MASS | 432996 | 43.558161 |
| 27752 | 2.791772 | |
| Retención 1r año MASS | 24156 | 2.430025 |
| Cultivación socios MID | 17102 | 1.720412 |
| Empresas y Colectivos Mass | 5923 | 0.595837 |
| Cultivación/conversión Donantes MASS | 5512 | 0.554491 |
| Mid+ Donors | 4415 | 0.444136 |
| Testamentarios | 781 | 0.078566 |
| Otros programas transversales | 688 | 0.069211 |
| Reactivación bajas MID | 372 | 0.037422 |
| Conversión prospectos | 218 | 0.021930 |
| Retención 1r año MID | 188 | 0.018912 |
| Prospectos Empresas & Colectivos Mass | 169 | 0.017001 |
| Cultivación/conversión Donantes MID | 143 | 0.014385 |
| Otros 12Few+ | 132 | 0.013279 |
| Reactivación/conversión EXDonantes MASS | 93 | 0.009356 |
| Empresas y Colectivos Mid, Mid + | 86 | 0.008651 |
| Públicos Especiales | 81 | 0.008148 |
| Potenciales a Major Donors | 56 | 0.005633 |
| Vehículo donación de Gran Donante = YES | 55 | 0.005533 |
| Major Donors | 42 | 0.004225 |
| Instituciones Públicas Mass | 39 | 0.003923 |
| Fundaciones Mass | 22 | 0.002213 |
| Empresas y Colectivos Estratégicas | 21 | 0.002113 |
| Otros 121 | 3 | 0.000302 |
| Fundaciones Mid, Mid + | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_programaherencias__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programaherencias__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_programaherencias__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 988546 | 99.444905 |
| True | 5518 | 0.555095 |
# Vamos a realizar analisis por cada variable
var = "msf_programais__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_programais__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_programais__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 993788 | 99.972235 |
| True | 276 | 0.027765 |
# Vamos a realizar analisis por cada variable
var = "msf_pressurecomplaint__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_pressurecomplaint__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 988990 | 99.48957 |
| True | 5074 | 0.51043 |
# Vamos a realizar analisis por cada variable
var = "msf_recencydonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencydonorcont__c es 727424. Lo que supone un 73.17677735035168% El nº de vacios para la variable msf_recencydonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 218.0 | 6535 | 2.450870 |
| 1102.0 | 6475 | 2.428368 |
| 128.0 | 5796 | 2.173717 |
| 1132.0 | 3950 | 1.481398 |
| 583.0 | 3579 | 1.342259 |
| ... | ... | ... |
| 4801.0 | 1 | 0.000375 |
| 11117.0 | 1 | 0.000375 |
| 4547.0 | 1 | 0.000375 |
| 5426.0 | 1 | 0.000375 |
| 4983.0 | 1 | 0.000375 |
8427 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_recencyrecurringdonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencyrecurringdonorcont__c es 59274. Lo que supone un 5.962795152022405% El nº de vacios para la variable msf_recencyrecurringdonorcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4.0 | 391646 | 41.896683 |
| 36.0 | 20951 | 2.241252 |
| 66.0 | 20310 | 2.172680 |
| 186.0 | 13427 | 1.436365 |
| 156.0 | 13005 | 1.391222 |
| 128.0 | 10337 | 1.105810 |
| 218.0 | 10233 | 1.094684 |
| 95.0 | 8499 | 0.909188 |
| 247.0 | 7675 | 0.821040 |
| 340.0 | 7139 | 0.763701 |
| 277.0 | 6996 | 0.748403 |
| 309.0 | 6101 | 0.652660 |
| 1983.0 | 5297 | 0.566651 |
| 2012.0 | 4219 | 0.451331 |
| 1648.0 | 3902 | 0.417420 |
| 2042.0 | 3888 | 0.415922 |
| 1314.0 | 3803 | 0.406829 |
| 1678.0 | 3790 | 0.405439 |
| 583.0 | 3755 | 0.401694 |
| 948.0 | 3667 | 0.392281 |
| 1955.0 | 3615 | 0.386718 |
| 1283.0 | 3555 | 0.380299 |
| 1769.0 | 3360 | 0.359439 |
| 1251.0 | 3341 | 0.357406 |
| 550.0 | 3333 | 0.356551 |
| 1740.0 | 3308 | 0.353876 |
| 1832.0 | 3256 | 0.348314 |
| 1922.0 | 3252 | 0.347886 |
| 914.0 | 3250 | 0.347672 |
| 1375.0 | 3210 | 0.343393 |
| 401.0 | 3201 | 0.342430 |
| 1405.0 | 3195 | 0.341788 |
| 1223.0 | 3187 | 0.340932 |
| 1618.0 | 3179 | 0.340076 |
| 1437.0 | 3175 | 0.339648 |
| 612.0 | 3165 | 0.338579 |
| 1802.0 | 3134 | 0.335262 |
| 858.0 | 3133 | 0.335155 |
| 2105.0 | 3127 | 0.334514 |
| 1863.0 | 3100 | 0.331625 |
| 1009.0 | 3095 | 0.331090 |
| 644.0 | 3092 | 0.330769 |
| 1468.0 | 3088 | 0.330342 |
| 766.0 | 3082 | 0.329700 |
| 368.0 | 3053 | 0.326597 |
| 2074.0 | 3053 | 0.326597 |
| 1709.0 | 3050 | 0.326276 |
| 886.0 | 3010 | 0.321997 |
| 1590.0 | 2989 | 0.319751 |
| 674.0 | 2986 | 0.319430 |
| 2378.0 | 2958 | 0.316435 |
| 1040.0 | 2954 | 0.316007 |
| 2410.0 | 2924 | 0.312798 |
| 493.0 | 2910 | 0.311300 |
| 1892.0 | 2902 | 0.310444 |
| 976.0 | 2890 | 0.309160 |
| 2136.0 | 2887 | 0.308839 |
| 1342.0 | 2887 | 0.308839 |
| 521.0 | 2851 | 0.304988 |
| 1559.0 | 2850 | 0.304881 |
| 827.0 | 2806 | 0.300174 |
| 431.0 | 2800 | 0.299533 |
| 462.0 | 2772 | 0.296537 |
| 795.0 | 2755 | 0.294719 |
| 736.0 | 2754 | 0.294612 |
| 1102.0 | 2751 | 0.294291 |
| 1528.0 | 2680 | 0.286695 |
| 704.0 | 2673 | 0.285947 |
| 2167.0 | 2618 | 0.280063 |
| 1496.0 | 2591 | 0.277175 |
| 2196.0 | 2528 | 0.270435 |
| 1069.0 | 2523 | 0.269900 |
| 1192.0 | 2511 | 0.268616 |
| 1131.0 | 2437 | 0.260700 |
| 2775.0 | 2431 | 0.260058 |
| 2469.0 | 2410 | 0.257812 |
| 2347.0 | 2408 | 0.257598 |
| 2319.0 | 2404 | 0.257170 |
| 2258.0 | 2376 | 0.254175 |
| 2228.0 | 2346 | 0.250965 |
| 1161.0 | 2302 | 0.246259 |
| 2287.0 | 2289 | 0.244868 |
| 2714.0 | 2283 | 0.244226 |
| 2439.0 | 2275 | 0.243370 |
| 3869.0 | 2256 | 0.241338 |
| 2742.0 | 2228 | 0.238342 |
| 2501.0 | 2219 | 0.237380 |
| 3109.0 | 2142 | 0.229142 |
| 3474.0 | 2139 | 0.228821 |
| 4205.0 | 2138 | 0.228714 |
| 3140.0 | 2119 | 0.226682 |
| 2532.0 | 2103 | 0.224970 |
| 2837.0 | 2095 | 0.224115 |
| 2563.0 | 2091 | 0.223687 |
| 3839.0 | 2055 | 0.219835 |
| 2654.0 | 2041 | 0.218338 |
| 2685.0 | 2040 | 0.218231 |
| 3932.0 | 2028 | 0.216947 |
| 2867.0 | 2026 | 0.216733 |
| 4175.0 | 2021 | 0.216198 |
| 2593.0 | 2016 | 0.215663 |
| 4237.0 | 2013 | 0.215342 |
| 3078.0 | 1989 | 0.212775 |
| 3809.0 | 1982 | 0.212026 |
| 3050.0 | 1980 | 0.211812 |
| 2804.0 | 1977 | 0.211491 |
| 3020.0 | 1970 | 0.210743 |
| 3442.0 | 1966 | 0.210315 |
| 2623.0 | 1932 | 0.206677 |
| 3505.0 | 1912 | 0.204538 |
| 2929.0 | 1903 | 0.203575 |
| 2958.0 | 1889 | 0.202077 |
| 3781.0 | 1834 | 0.196194 |
| 2896.0 | 1822 | 0.194910 |
| 4114.0 | 1812 | 0.193840 |
| 4146.0 | 1811 | 0.193733 |
| 3566.0 | 1791 | 0.191594 |
| 4083.0 | 1788 | 0.191273 |
| 3900.0 | 1780 | 0.190417 |
| 3414.0 | 1780 | 0.190417 |
| 3201.0 | 1767 | 0.189026 |
| 3960.0 | 1761 | 0.188385 |
| 4023.0 | 1753 | 0.187529 |
| 2987.0 | 1736 | 0.185710 |
| 3749.0 | 1710 | 0.182929 |
| 3596.0 | 1670 | 0.178650 |
| 3230.0 | 1660 | 0.177580 |
| 3993.0 | 1634 | 0.174799 |
| 3384.0 | 1633 | 0.174692 |
| 3351.0 | 1630 | 0.174371 |
| 3263.0 | 1628 | 0.174157 |
| 3293.0 | 1607 | 0.171910 |
| 3169.0 | 1603 | 0.171482 |
| 3659.0 | 1585 | 0.169557 |
| 4601.0 | 1539 | 0.164636 |
| 4569.0 | 1539 | 0.164636 |
| 4512.0 | 1533 | 0.163994 |
| 3719.0 | 1526 | 0.163245 |
| 4051.0 | 1517 | 0.162282 |
| 4266.0 | 1482 | 0.158538 |
| 3320.0 | 1475 | 0.157789 |
| 4295.0 | 1435 | 0.153510 |
| 4327.0 | 1429 | 0.152869 |
| 4540.0 | 1424 | 0.152334 |
| 3533.0 | 1422 | 0.152120 |
| 4390.0 | 1389 | 0.148590 |
| 4420.0 | 1369 | 0.146450 |
| 3627.0 | 1365 | 0.146022 |
| 4481.0 | 1352 | 0.144631 |
| 3687.0 | 1345 | 0.143883 |
| 4450.0 | 1275 | 0.136394 |
| 5268.0 | 1271 | 0.135966 |
| 5300.0 | 1247 | 0.133399 |
| 4358.0 | 1239 | 0.132543 |
| 4660.0 | 1216 | 0.130083 |
| 5332.0 | 1207 | 0.129120 |
| 5392.0 | 1188 | 0.127087 |
| 4692.0 | 1180 | 0.126232 |
| 4966.0 | 1179 | 0.126125 |
| 4631.0 | 1175 | 0.125697 |
| 5423.0 | 1155 | 0.123557 |
| 4723.0 | 1097 | 0.117353 |
| 4755.0 | 1082 | 0.115748 |
| 4814.0 | 1067 | 0.114143 |
| 5665.0 | 1066 | 0.114036 |
| 5605.0 | 1043 | 0.111576 |
| 4784.0 | 1041 | 0.111362 |
| 4846.0 | 1035 | 0.110720 |
| 5240.0 | 1033 | 0.110506 |
| 5633.0 | 1029 | 0.110078 |
| 4905.0 | 1028 | 0.109971 |
| 4877.0 | 1025 | 0.109650 |
| 5210.0 | 1024 | 0.109543 |
| 5027.0 | 1023 | 0.109436 |
| 4933.0 | 1022 | 0.109329 |
| 5697.0 | 985 | 0.105371 |
| 4996.0 | 967 | 0.103446 |
| 5482.0 | 947 | 0.101306 |
| 5360.0 | 945 | 0.101092 |
| 5057.0 | 940 | 0.100557 |
| 5545.0 | 927 | 0.099167 |
| 5573.0 | 895 | 0.095743 |
| 5178.0 | 895 | 0.095743 |
| 5514.0 | 867 | 0.092748 |
| 5941.0 | 863 | 0.092320 |
| 5147.0 | 847 | 0.090609 |
| 5119.0 | 845 | 0.090395 |
| 5756.0 | 842 | 0.090074 |
| 5727.0 | 836 | 0.089432 |
| 5848.0 | 809 | 0.086544 |
| 5787.0 | 806 | 0.086223 |
| 6029.0 | 772 | 0.082585 |
| 5972.0 | 769 | 0.082264 |
| 5087.0 | 760 | 0.081302 |
| 5447.0 | 750 | 0.080232 |
| 5997.0 | 717 | 0.076702 |
| 5909.0 | 702 | 0.075097 |
| 5819.0 | 700 | 0.074883 |
| 5877.0 | 646 | 0.069106 |
| 7738.0 | 643 | 0.068786 |
| 6123.0 | 628 | 0.067181 |
| 6364.0 | 603 | 0.064506 |
| 6393.0 | 597 | 0.063865 |
| 6062.0 | 571 | 0.061083 |
| 6151.0 | 542 | 0.057981 |
| 6426.0 | 532 | 0.056911 |
| 6336.0 | 521 | 0.055734 |
| 6245.0 | 521 | 0.055734 |
| 6305.0 | 520 | 0.055627 |
| 6214.0 | 519 | 0.055520 |
| 6091.0 | 518 | 0.055414 |
| 6274.0 | 508 | 0.054344 |
| 6184.0 | 495 | 0.052953 |
| 6487.0 | 448 | 0.047925 |
| 6669.0 | 443 | 0.047390 |
| 6518.0 | 439 | 0.046962 |
| 6700.0 | 428 | 0.045786 |
| 6549.0 | 423 | 0.045251 |
| 6728.0 | 418 | 0.044716 |
| 6456.0 | 407 | 0.043539 |
| 6639.0 | 396 | 0.042362 |
| 6759.0 | 390 | 0.041721 |
| 6609.0 | 385 | 0.041186 |
| 6882.0 | 380 | 0.040651 |
| 6578.0 | 358 | 0.038297 |
| 7097.0 | 355 | 0.037976 |
| 7068.0 | 329 | 0.035195 |
| 7128.0 | 323 | 0.034553 |
| 6946.0 | 320 | 0.034232 |
| 6849.0 | 315 | 0.033697 |
| 7037.0 | 307 | 0.032842 |
| 7493.0 | 286 | 0.030595 |
| 6820.0 | 286 | 0.030595 |
| 7462.0 | 277 | 0.029632 |
| 6789.0 | 273 | 0.029204 |
| 8315.0 | 267 | 0.028563 |
| 7007.0 | 263 | 0.028135 |
| 7585.0 | 254 | 0.027172 |
| 7950.0 | 250 | 0.026744 |
| 8192.0 | 242 | 0.025888 |
| 7403.0 | 236 | 0.025246 |
| 6976.0 | 236 | 0.025246 |
| 7159.0 | 235 | 0.025139 |
| 7858.0 | 232 | 0.024818 |
| 7220.0 | 215 | 0.023000 |
| 7312.0 | 207 | 0.022144 |
| 7615.0 | 207 | 0.022144 |
| 7250.0 | 207 | 0.022144 |
| 6915.0 | 205 | 0.021930 |
| 7434.0 | 203 | 0.021716 |
| 7189.0 | 200 | 0.021395 |
| 8042.0 | 200 | 0.021395 |
| 9776.0 | 194 | 0.020753 |
| 7342.0 | 187 | 0.020004 |
| 8133.0 | 187 | 0.020004 |
| 7646.0 | 186 | 0.019898 |
| 8223.0 | 185 | 0.019791 |
| 7373.0 | 183 | 0.019577 |
| 8164.0 | 178 | 0.019042 |
| 7281.0 | 176 | 0.018828 |
| 7524.0 | 171 | 0.018293 |
| 7889.0 | 171 | 0.018293 |
| 7554.0 | 169 | 0.018079 |
| 7827.0 | 165 | 0.017651 |
| 9684.0 | 162 | 0.017330 |
| 9411.0 | 162 | 0.017330 |
| 8254.0 | 157 | 0.016795 |
| 8494.0 | 156 | 0.016688 |
| 8407.0 | 154 | 0.016474 |
| 9288.0 | 154 | 0.016474 |
| 7768.0 | 153 | 0.016367 |
| 7980.0 | 152 | 0.016260 |
| 7919.0 | 151 | 0.016153 |
| 9653.0 | 148 | 0.015832 |
| 8923.0 | 146 | 0.015618 |
| 10141.0 | 146 | 0.015618 |
| 8558.0 | 145 | 0.015512 |
| 7799.0 | 145 | 0.015512 |
| 9594.0 | 143 | 0.015298 |
| 9959.0 | 143 | 0.015298 |
| 8954.0 | 136 | 0.014549 |
| 9868.0 | 133 | 0.014228 |
| 8345.0 | 131 | 0.014014 |
| 10019.0 | 130 | 0.013907 |
| 8072.0 | 130 | 0.013907 |
| 9503.0 | 129 | 0.013800 |
| 8773.0 | 129 | 0.013800 |
| 8468.0 | 124 | 0.013265 |
| 8864.0 | 123 | 0.013158 |
| 9319.0 | 122 | 0.013051 |
| 8011.0 | 121 | 0.012944 |
| 9229.0 | 120 | 0.012837 |
| 9046.0 | 120 | 0.012837 |
| 8376.0 | 118 | 0.012623 |
| 8103.0 | 117 | 0.012516 |
| 8284.0 | 114 | 0.012195 |
| 2198.0 | 110 | 0.011767 |
| 8437.0 | 109 | 0.011660 |
| 9260.0 | 108 | 0.011553 |
| 10049.0 | 107 | 0.011446 |
| 8577.0 | 107 | 0.011446 |
| 8681.0 | 107 | 0.011446 |
| 10384.0 | 107 | 0.011446 |
| 7668.0 | 104 | 0.011125 |
| 8620.0 | 101 | 0.010805 |
| 8521.0 | 100 | 0.010698 |
| 10234.0 | 98 | 0.010484 |
| 9138.0 | 97 | 0.010377 |
| 10325.0 | 91 | 0.009735 |
| 8895.0 | 90 | 0.009628 |
| 8985.0 | 89 | 0.009521 |
| 9625.0 | 85 | 0.009093 |
| 10414.0 | 84 | 0.008986 |
| 7665.0 | 83 | 0.008879 |
| 8803.0 | 82 | 0.008772 |
| 2379.0 | 77 | 0.008237 |
| 8711.0 | 75 | 0.008023 |
| 9990.0 | 73 | 0.007809 |
| 2045.0 | 72 | 0.007702 |
| 8742.0 | 70 | 0.007488 |
| 9715.0 | 68 | 0.007274 |
| 8650.0 | 68 | 0.007274 |
| 2014.0 | 67 | 0.007167 |
| 2776.0 | 67 | 0.007167 |
| 2990.0 | 66 | 0.007060 |
| 2348.0 | 66 | 0.007060 |
| 7695.0 | 66 | 0.007060 |
| 3079.0 | 65 | 0.006953 |
| 2259.0 | 65 | 0.006953 |
| 2075.0 | 64 | 0.006846 |
| 2320.0 | 63 | 0.006739 |
| 2624.0 | 63 | 0.006739 |
| 2898.0 | 62 | 0.006633 |
| 2471.0 | 61 | 0.006526 |
| 2959.0 | 59 | 0.006312 |
| 10111.0 | 59 | 0.006312 |
| 9533.0 | 59 | 0.006312 |
| 9076.0 | 58 | 0.006205 |
| 2806.0 | 57 | 0.006098 |
| 10356.0 | 57 | 0.006098 |
| 9168.0 | 55 | 0.005884 |
| 3051.0 | 55 | 0.005884 |
| 9805.0 | 55 | 0.005884 |
| 3416.0 | 54 | 0.005777 |
| 9745.0 | 54 | 0.005777 |
| 2289.0 | 53 | 0.005670 |
| 2745.0 | 53 | 0.005670 |
| 1741.0 | 52 | 0.005563 |
| 3141.0 | 52 | 0.005563 |
| 10502.0 | 52 | 0.005563 |
| 3110.0 | 52 | 0.005563 |
| 8834.0 | 52 | 0.005563 |
| 10264.0 | 52 | 0.005563 |
| 2440.0 | 51 | 0.005456 |
| 1924.0 | 50 | 0.005349 |
| 3232.0 | 50 | 0.005349 |
| 9107.0 | 50 | 0.005349 |
| 2106.0 | 50 | 0.005349 |
| 3506.0 | 49 | 0.005242 |
| 1680.0 | 48 | 0.005135 |
| 3597.0 | 48 | 0.005135 |
| 1833.0 | 47 | 0.005028 |
| 9015.0 | 47 | 0.005028 |
| 10081.0 | 47 | 0.005028 |
| 3355.0 | 46 | 0.004921 |
| 9441.0 | 46 | 0.004921 |
| 1315.0 | 46 | 0.004921 |
| 4085.0 | 45 | 0.004814 |
| 9380.0 | 45 | 0.004814 |
| 5270.0 | 45 | 0.004814 |
| 10507.0 | 45 | 0.004814 |
| 4115.0 | 44 | 0.004707 |
| 1253.0 | 43 | 0.004600 |
| 9472.0 | 43 | 0.004600 |
| 9896.0 | 42 | 0.004493 |
| 1710.0 | 41 | 0.004386 |
| 9350.0 | 40 | 0.004279 |
| 9564.0 | 40 | 0.004279 |
| 10596.0 | 39 | 0.004172 |
| 1224.0 | 39 | 0.004172 |
| 3536.0 | 39 | 0.004172 |
| 10166.0 | 38 | 0.004065 |
| 4024.0 | 38 | 0.004065 |
| 9929.0 | 38 | 0.004065 |
| 10476.0 | 37 | 0.003958 |
| 9199.0 | 37 | 0.003958 |
| 3202.0 | 37 | 0.003958 |
| 3171.0 | 36 | 0.003851 |
| 10749.0 | 36 | 0.003851 |
| 3294.0 | 35 | 0.003744 |
| 1284.0 | 35 | 0.003744 |
| 5393.0 | 35 | 0.003744 |
| 3962.0 | 35 | 0.003744 |
| 9836.0 | 34 | 0.003637 |
| 3385.0 | 34 | 0.003637 |
| 1163.0 | 34 | 0.003637 |
| 10443.0 | 34 | 0.003637 |
| 1132.0 | 34 | 0.003637 |
| 3324.0 | 33 | 0.003530 |
| 3567.0 | 33 | 0.003530 |
| 4451.0 | 33 | 0.003530 |
| 1529.0 | 32 | 0.003423 |
| 10694.0 | 32 | 0.003423 |
| 1193.0 | 32 | 0.003423 |
| 1345.0 | 31 | 0.003316 |
| 4602.0 | 31 | 0.003316 |
| 10774.0 | 30 | 0.003209 |
| 1406.0 | 30 | 0.003209 |
| 5242.0 | 30 | 0.003209 |
| 1771.0 | 30 | 0.003209 |
| 3444.0 | 30 | 0.003209 |
| 3720.0 | 30 | 0.003209 |
| 1894.0 | 30 | 0.003209 |
| 7703.0 | 29 | 0.003102 |
| 1376.0 | 29 | 0.003102 |
| 4206.0 | 29 | 0.003102 |
| 3840.0 | 28 | 0.002995 |
| 4571.0 | 27 | 0.002888 |
| 3901.0 | 27 | 0.002888 |
| 10964.0 | 27 | 0.002888 |
| 10294.0 | 27 | 0.002888 |
| 1649.0 | 27 | 0.002888 |
| 5211.0 | 27 | 0.002888 |
| 5301.0 | 26 | 0.002781 |
| 5058.0 | 26 | 0.002781 |
| 5089.0 | 26 | 0.002781 |
| 7700.0 | 26 | 0.002781 |
| 11051.0 | 26 | 0.002781 |
| 4816.0 | 25 | 0.002674 |
| 4298.0 | 25 | 0.002674 |
| 4267.0 | 25 | 0.002674 |
| 4359.0 | 25 | 0.002674 |
| 3871.0 | 25 | 0.002674 |
| 10869.0 | 24 | 0.002567 |
| 5515.0 | 24 | 0.002567 |
| 3750.0 | 24 | 0.002567 |
| 5576.0 | 24 | 0.002567 |
| 5636.0 | 23 | 0.002460 |
| 3689.0 | 23 | 0.002460 |
| 1071.0 | 23 | 0.002460 |
| 10203.0 | 22 | 0.002353 |
| 10960.0 | 22 | 0.002353 |
| 5028.0 | 21 | 0.002246 |
| 4632.0 | 21 | 0.002246 |
| 3475.0 | 20 | 0.002140 |
| 1498.0 | 20 | 0.002140 |
| 5362.0 | 20 | 0.002140 |
| 5485.0 | 20 | 0.002140 |
| 4054.0 | 20 | 0.002140 |
| 4328.0 | 20 | 0.002140 |
| 3628.0 | 19 | 0.002033 |
| 5607.0 | 19 | 0.002033 |
| 5728.0 | 19 | 0.002033 |
| 11359.0 | 18 | 0.001926 |
| 10142.0 | 18 | 0.001926 |
| 4785.0 | 18 | 0.001926 |
| 6428.0 | 18 | 0.001926 |
| 883.0 | 18 | 0.001926 |
| 4693.0 | 18 | 0.001926 |
| 5150.0 | 18 | 0.001926 |
| 11206.0 | 17 | 0.001819 |
| 10743.0 | 17 | 0.001819 |
| 5820.0 | 17 | 0.001819 |
| 5454.0 | 17 | 0.001819 |
| 4724.0 | 17 | 0.001819 |
| 4936.0 | 17 | 0.001819 |
| 5181.0 | 17 | 0.001819 |
| 11086.0 | 16 | 0.001712 |
| 10834.0 | 16 | 0.001712 |
| 10537.0 | 16 | 0.001712 |
| 6307.0 | 16 | 0.001712 |
| 4967.0 | 16 | 0.001712 |
| 6093.0 | 16 | 0.001712 |
| 10718.0 | 15 | 0.001605 |
| 6216.0 | 15 | 0.001605 |
| 10599.0 | 15 | 0.001605 |
| 5546.0 | 15 | 0.001605 |
| 8589.0 | 14 | 0.001498 |
| 11139.0 | 14 | 0.001498 |
| 10415.0 | 14 | 0.001498 |
| 11176.0 | 14 | 0.001498 |
| 11145.0 | 14 | 0.001498 |
| 5973.0 | 14 | 0.001498 |
| 5759.0 | 13 | 0.001391 |
| 6550.0 | 13 | 0.001391 |
| 5120.0 | 13 | 0.001391 |
| 5667.0 | 13 | 0.001391 |
| 9898.0 | 13 | 0.001391 |
| 11025.0 | 13 | 0.001391 |
| 10721.0 | 13 | 0.001391 |
| 6124.0 | 12 | 0.001284 |
| 8529.0 | 12 | 0.001284 |
| 10050.0 | 12 | 0.001284 |
| 6277.0 | 12 | 0.001284 |
| 6154.0 | 11 | 0.001177 |
| 5942.0 | 11 | 0.001177 |
| 4663.0 | 11 | 0.001177 |
| 6246.0 | 11 | 0.001177 |
| 5881.0 | 10 | 0.001070 |
| 6032.0 | 10 | 0.001070 |
| 5789.0 | 10 | 0.001070 |
| 5698.0 | 10 | 0.001070 |
| 10929.0 | 10 | 0.001070 |
| 4997.0 | 10 | 0.001070 |
| 6458.0 | 9 | 0.000963 |
| 10841.0 | 9 | 0.000963 |
| 10780.0 | 9 | 0.000963 |
| 10624.0 | 9 | 0.000963 |
| 6519.0 | 9 | 0.000963 |
| 6001.0 | 8 | 0.000856 |
| 11329.0 | 8 | 0.000856 |
| 10568.0 | 8 | 0.000856 |
| 11098.0 | 8 | 0.000856 |
| 10805.0 | 8 | 0.000856 |
| 11419.0 | 7 | 0.000749 |
| 5851.0 | 7 | 0.000749 |
| 11224.0 | 7 | 0.000749 |
| 10887.0 | 7 | 0.000749 |
| 10652.0 | 7 | 0.000749 |
| 5912.0 | 7 | 0.000749 |
| 10811.0 | 6 | 0.000642 |
| 11163.0 | 6 | 0.000642 |
| 6489.0 | 6 | 0.000642 |
| 5.0 | 6 | 0.000642 |
| 11510.0 | 6 | 0.000642 |
| 6185.0 | 6 | 0.000642 |
| 6063.0 | 6 | 0.000642 |
| 10172.0 | 5 | 0.000535 |
| 9837.0 | 5 | 0.000535 |
| 10629.0 | 5 | 0.000535 |
| 11857.0 | 5 | 0.000535 |
| 10295.0 | 5 | 0.000535 |
| 10690.0 | 4 | 0.000428 |
| 11079.0 | 4 | 0.000428 |
| 11856.0 | 4 | 0.000428 |
| 11542.0 | 4 | 0.000428 |
| 11114.0 | 4 | 0.000428 |
| 10446.0 | 4 | 0.000428 |
| 7667.0 | 4 | 0.000428 |
| 10933.0 | 3 | 0.000321 |
| 11267.0 | 3 | 0.000321 |
| 11633.0 | 3 | 0.000321 |
| 11029.0 | 3 | 0.000321 |
| 6397.0 | 3 | 0.000321 |
| 11237.0 | 3 | 0.000321 |
| 9806.0 | 3 | 0.000321 |
| 11285.0 | 3 | 0.000321 |
| 11695.0 | 3 | 0.000321 |
| 37.0 | 3 | 0.000321 |
| 11826.0 | 3 | 0.000321 |
| 11298.0 | 2 | 0.000214 |
| 248.0 | 2 | 0.000214 |
| 11479.0 | 2 | 0.000214 |
| 11567.0 | 2 | 0.000214 |
| 11055.0 | 2 | 0.000214 |
| 11076.0 | 2 | 0.000214 |
| 10660.0 | 2 | 0.000214 |
| 463.0 | 2 | 0.000214 |
| 796.0 | 2 | 0.000214 |
| 11664.0 | 2 | 0.000214 |
| 11358.0 | 2 | 0.000214 |
| 11238.0 | 2 | 0.000214 |
| 7690.0 | 2 | 0.000214 |
| 11772.0 | 2 | 0.000214 |
| 11103.0 | 2 | 0.000214 |
| 10902.0 | 2 | 0.000214 |
| 11370.0 | 2 | 0.000214 |
| 11572.0 | 1 | 0.000107 |
| 8698.0 | 1 | 0.000107 |
| 11603.0 | 1 | 0.000107 |
| 9561.0 | 1 | 0.000107 |
| 11476.0 | 1 | 0.000107 |
| 11867.0 | 1 | 0.000107 |
| 11723.0 | 1 | 0.000107 |
| 10572.0 | 1 | 0.000107 |
| 11631.0 | 1 | 0.000107 |
| 767.0 | 1 | 0.000107 |
| 11888.0 | 1 | 0.000107 |
| 11968.0 | 1 | 0.000107 |
| 705.0 | 1 | 0.000107 |
| 11450.0 | 1 | 0.000107 |
| 11937.0 | 1 | 0.000107 |
| 11873.0 | 1 | 0.000107 |
| 8731.0 | 1 | 0.000107 |
| 11420.0 | 1 | 0.000107 |
| 1734.0 | 1 | 0.000107 |
| 11793.0 | 1 | 0.000107 |
| 10994.0 | 1 | 0.000107 |
| 11037.0 | 1 | 0.000107 |
| 11022.0 | 1 | 0.000107 |
| 219.0 | 1 | 0.000107 |
| 11053.0 | 1 | 0.000107 |
| 11026.0 | 1 | 0.000107 |
| 310.0 | 1 | 0.000107 |
| 67.0 | 1 | 0.000107 |
| 949.0 | 1 | 0.000107 |
| 7697.0 | 1 | 0.000107 |
| 198.0 | 1 | 0.000107 |
| 1006.0 | 1 | 0.000107 |
| 432.0 | 1 | 0.000107 |
# Vamos a realizar analisis por cada variable
var = "msf_recencytotalcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_recencytotalcont__c es 57038. Lo que supone un 5.73785993658356% El nº de vacios para la variable msf_recencytotalcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4.0 | 392601 | 41.898624 |
| 36.0 | 21155 | 2.257675 |
| 66.0 | 20464 | 2.183931 |
| 186.0 | 12876 | 1.374135 |
| 156.0 | 12486 | 1.332514 |
| ... | ... | ... |
| 598.0 | 1 | 0.000107 |
| 9228.0 | 1 | 0.000107 |
| 4250.0 | 1 | 0.000107 |
| 4469.0 | 1 | 0.000107 |
| 896.0 | 1 | 0.000107 |
4156 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrfvdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 727424 | 73.176777 |
| 1.4 | 27938 | 2.810483 |
| 1.2 | 25594 | 2.574683 |
| 1.0 | 22498 | 2.263235 |
| 1.8 | 22083 | 2.221487 |
| 1.5 | 19069 | 1.918287 |
| 1.6 | 15558 | 1.565090 |
| 1.7 | 14356 | 1.444173 |
| 2.3 | 11514 | 1.158276 |
| 1.9 | 10863 | 1.092787 |
| 2.0 | 8179 | 0.822784 |
| 2.1 | 8038 | 0.808600 |
| 2.2 | 7516 | 0.756088 |
| 2.8 | 6967 | 0.700860 |
| 2.5 | 5408 | 0.544029 |
| 2.4 | 5165 | 0.519584 |
| 3.0 | 5128 | 0.515862 |
| 2.6 | 4798 | 0.482665 |
| 3.3 | 4340 | 0.436592 |
| 3.2 | 3851 | 0.387400 |
| 3.8 | 3612 | 0.363357 |
| 2.7 | 3510 | 0.353096 |
| 3.6 | 3167 | 0.318591 |
| 3.5 | 3047 | 0.306519 |
| 4.1 | 2964 | 0.298170 |
| 2.9 | 2424 | 0.243847 |
| 3.4 | 2418 | 0.243244 |
| 3.1 | 2191 | 0.220408 |
| 3.9 | 2143 | 0.215580 |
| 3.7 | 2011 | 0.202301 |
| 4.4 | 1451 | 0.145966 |
| 4.0 | 1155 | 0.116190 |
| 4.2 | 1111 | 0.111763 |
| 4.3 | 1027 | 0.103313 |
| 1.3 | 967 | 0.097277 |
| 4.6 | 787 | 0.079170 |
| 4.7 | 592 | 0.059554 |
| 4.9 | 498 | 0.050097 |
| 4.5 | 492 | 0.049494 |
| 4.8 | 473 | 0.047582 |
| 5.0 | 366 | 0.036819 |
| 5.1 | 339 | 0.034102 |
| 5.2 | 221 | 0.022232 |
| 5.4 | 162 | 0.016297 |
| 5.5 | 133 | 0.013379 |
| 5.3 | 93 | 0.009356 |
| 6.0 | 82 | 0.008249 |
| 5.7 | 74 | 0.007444 |
| 5.6 | 73 | 0.007344 |
| 5.9 | 45 | 0.004527 |
| 5.8 | 32 | 0.003219 |
| 6.5 | 26 | 0.002616 |
| 0.8 | 16 | 0.001610 |
| 6.2 | 12 | 0.001207 |
| 0.4 | 11 | 0.001107 |
| 0.6 | 10 | 0.001006 |
| 6.1 | 9 | 0.000905 |
| 0.5 | 6 | 0.000604 |
| 6.3 | 4 | 0.000402 |
| 6.7 | 4 | 0.000402 |
| 6.6 | 3 | 0.000302 |
| 6.4 | 3 | 0.000302 |
| 0.2 | 3 | 0.000302 |
| 0.7 | 3 | 0.000302 |
| 1.1 | 2 | 0.000201 |
| 7.0 | 2 | 0.000201 |
| 6.8 | 2 | 0.000201 |
| 0.9 | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrfvrecurringdonor__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrfvrecurringdonor__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 5.0 | 131141 | 13.192410 |
| 4.5 | 97173 | 9.775326 |
| 3.5 | 76494 | 7.695078 |
| 0.0 | 58415 | 5.876382 |
| 0.4 | 57835 | 5.818036 |
| 0.2 | 43619 | 4.387947 |
| 3.0 | 38256 | 3.848444 |
| 0.6 | 32846 | 3.304214 |
| 1.9 | 29234 | 2.940857 |
| 2.1 | 28489 | 2.865912 |
| 1.7 | 27945 | 2.811187 |
| 1.0 | 27282 | 2.744491 |
| 0.8 | 25872 | 2.602649 |
| 0.5 | 21279 | 2.140607 |
| 0.7 | 21045 | 2.117067 |
| 2.0 | 20994 | 2.111936 |
| 4.7 | 20535 | 2.065762 |
| 4.2 | 18389 | 1.849881 |
| 1.4 | 18145 | 1.825335 |
| 1.5 | 15185 | 1.527568 |
| 1.8 | 14527 | 1.461375 |
| 0.9 | 14394 | 1.447995 |
| 2.5 | 13841 | 1.392365 |
| 1.6 | 13796 | 1.387838 |
| 3.2 | 13232 | 1.331101 |
| 1.1 | 13128 | 1.320639 |
| 2.3 | 11952 | 1.202337 |
| 5.5 | 11441 | 1.150932 |
| 4.0 | 11273 | 1.134032 |
| 1.2 | 11248 | 1.131517 |
| 1.3 | 9431 | 0.948732 |
| 4.4 | 9403 | 0.945915 |
| 3.9 | 8215 | 0.826406 |
| 2.9 | 5830 | 0.586481 |
| 2.7 | 5153 | 0.518377 |
| 2.2 | 4636 | 0.466368 |
| 2.4 | 2281 | 0.229462 |
| 6.0 | 2103 | 0.211556 |
| 3.7 | 1697 | 0.170713 |
| 3.6 | 1357 | 0.136510 |
| 4.1 | 1343 | 0.135102 |
| 2.6 | 1061 | 0.106734 |
| 3.4 | 753 | 0.075750 |
| 5.2 | 752 | 0.075649 |
| 4.9 | 371 | 0.037322 |
| 3.1 | 146 | 0.014687 |
| 5.7 | 143 | 0.014385 |
| 6.5 | 92 | 0.009255 |
| 4.6 | 75 | 0.007545 |
| 5.4 | 67 | 0.006740 |
| 2.8 | 45 | 0.004527 |
| 3.3 | 31 | 0.003119 |
| 4.3 | 25 | 0.002515 |
| 3.8 | 18 | 0.001811 |
| 5.1 | 12 | 0.001207 |
| 6.2 | 6 | 0.000604 |
| 4.8 | 4 | 0.000402 |
| 5.9 | 3 | 0.000302 |
| 7.0 | 2 | 0.000201 |
| 5.6 | 2 | 0.000201 |
| 6.1 | 1 | 0.000101 |
| 6.7 | 1 | 0.000101 |
# Vamos a realizar analisis por cada variable
var = "msf_scoringrvtotal__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_scoringrvtotal__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 5.0 | 193805 | 19.496230 |
| 4.2 | 170212 | 17.122841 |
| 2.6 | 105584 | 10.621449 |
| 1.8 | 75919 | 7.637235 |
| 0.0 | 56220 | 5.655571 |
| 3.4 | 51904 | 5.221394 |
| 1.2 | 36141 | 3.635681 |
| 2.0 | 34384 | 3.458932 |
| 3.6 | 31987 | 3.217801 |
| 1.0 | 28277 | 2.844585 |
| 1.4 | 26823 | 2.698317 |
| 3.8 | 26673 | 2.683228 |
| 4.6 | 25861 | 2.601543 |
| 4.4 | 25238 | 2.538871 |
| 2.2 | 25228 | 2.537865 |
| 5.8 | 18119 | 1.822720 |
| 1.6 | 11432 | 1.150027 |
| 4.8 | 9145 | 0.919961 |
| 4.0 | 8318 | 0.836767 |
| 2.4 | 7984 | 0.803168 |
| 2.8 | 6860 | 0.690096 |
| 3.0 | 5502 | 0.553485 |
| 6.6 | 4070 | 0.409430 |
| 3.2 | 2261 | 0.227450 |
| 5.2 | 1890 | 0.190129 |
| 5.4 | 1787 | 0.179767 |
| 5.6 | 763 | 0.076756 |
| 6.0 | 487 | 0.048991 |
| 6.2 | 431 | 0.043357 |
| 7.4 | 343 | 0.034505 |
| 6.4 | 153 | 0.015391 |
| 0.8 | 74 | 0.007444 |
| 8.2 | 43 | 0.004326 |
| 6.8 | 33 | 0.003320 |
| 7.0 | 28 | 0.002817 |
| 0.6 | 26 | 0.002616 |
| 0.4 | 21 | 0.002113 |
| 7.2 | 13 | 0.001308 |
| 0.2 | 10 | 0.001006 |
| 7.8 | 8 | 0.000805 |
| 7.6 | 4 | 0.000402 |
| 8.0 | 3 | 0.000302 |
# Vamos a realizar analisis por cada variable
var = "msf_mailingsegment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_mailingsegment__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_mailingsegment__c es 7. Lo que supone un 0.0007041800125545236%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| SOC NO REC SIN EXTRA | 313671 | 31.554407 |
| BAJAS ANTIGUAS | 154299 | 15.522039 |
| BAJAS MUY ANTIGUAS | 134593 | 13.539671 |
| BAJAS NO REC | 126262 | 12.701597 |
| BAJAS ACT | 50271 | 5.057119 |
| SOC CON EXTRA ACT | 47970 | 4.825645 |
| BAJAS REC | 41715 | 4.196410 |
| SOC CON EXTRA NO REC | 38684 | 3.891500 |
| SOC NUEVOS | 29424 | 2.959970 |
| SOC CON EXTRA REC | 28789 | 2.896091 |
| SOC REC SIN EXTRA | 21233 | 2.135979 |
| EMPRESAS NO SOCIAS | 3935 | 0.395850 |
| EMPRESAS SOCIAS | 2381 | 0.239522 |
| No se está calculando la cadencia de donante | 513 | 0.051606 |
| DON MUY ANTIGUOS | 155 | 0.015593 |
| No cumple ninguno de los criterios anteriores | 68 | 0.006841 |
| DON ANTIGUOS | 29 | 0.002917 |
| DON OCA REC | 16 | 0.001610 |
| DON OCA ACT | 13 | 0.001308 |
| DON UNICO NO REC | 12 | 0.001207 |
| DON OCA NO REC | 12 | 0.001207 |
| 7 | 0.000704 | |
| DON UNICO REC | 4 | 0.000402 |
| DON 1R AÑO | 3 | 0.000302 |
| DON PS ACT | 3 | 0.000302 |
| DON PS REC | 2 | 0.000201 |
# Vamos a realizar analisis por cada variable
var = "msf_membertype__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_membertype__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_membertype__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Baja | 425726 | 42.826820 |
| Socio | 301335 | 30.313441 |
| Socio + Exdonante | 132714 | 13.350649 |
| Baja + Exdonante | 79431 | 7.990532 |
| Socio + Donante | 48175 | 4.846267 |
| Baja + Donante | 5923 | 0.595837 |
| Nada | 503 | 0.050600 |
| Exdonante | 229 | 0.023037 |
| Donante | 18 | 0.001811 |
| Nada (Donante SMS) | 10 | 0.001006 |
# Vamos a realizar analisis por cada variable
var = "npo02__totaloppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__totaloppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.00 | 57038 | 5.737860 |
| 30.00 | 12590 | 1.266518 |
| 60.00 | 12133 | 1.220545 |
| 10.00 | 11459 | 1.152743 |
| 20.00 | 11239 | 1.130611 |
| ... | ... | ... |
| 6708.69 | 1 | 0.000101 |
| 25246.64 | 1 | 0.000101 |
| 554.89 | 1 | 0.000101 |
| 4367.70 | 1 | 0.000101 |
| 1628.70 | 1 | 0.000101 |
84077 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountthisyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamountthisyear__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 994064 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__oppamount2yearsago__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamount2yearsago__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 994064 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__oppamountlastyear__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0% El nº de vacios para la variable npo02__oppamountlastyear__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 994064 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "npo02__best_gift_year_total__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable npo02__best_gift_year_total__c es 57038. Lo que supone un 5.73785993658356% El nº de vacios para la variable npo02__best_gift_year_total__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.00 | 114919 | 12.264227 |
| 180.00 | 57679 | 6.155539 |
| 60.00 | 55023 | 5.872089 |
| 240.00 | 38835 | 4.144495 |
| 144.00 | 26031 | 2.778045 |
| ... | ... | ... |
| 3930.00 | 1 | 0.000107 |
| 857.00 | 1 | 0.000107 |
| 312.02 | 1 | 0.000107 |
| 790.40 | 1 | 0.000107 |
| 161.01 | 1 | 0.000107 |
7035 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_totalfiscaloppamount__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_totalfiscaloppamount__c es 3. Lo que supone un 0.0003017914339519387% El nº de vacios para la variable msf_totalfiscaloppamount__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.00 | 56551 | 5.688886 |
| 30.00 | 12607 | 1.268232 |
| 60.00 | 12131 | 1.220348 |
| 10.00 | 11532 | 1.160090 |
| 20.00 | 11273 | 1.134035 |
| ... | ... | ... |
| 7055.63 | 1 | 0.000101 |
| 4528.35 | 1 | 0.000101 |
| 1450.25 | 1 | 0.000101 |
| 5346.25 | 1 | 0.000101 |
| 1628.70 | 1 | 0.000101 |
84207 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastannualizedquota__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastannualizedquota__c es 41302. Lo que supone un 4.154863268360991% El nº de vacios para la variable msf_lastannualizedquota__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 1.200000e+02 | 205240 | 21.541581 |
| 1.800000e+02 | 103733 | 10.887609 |
| 6.000000e+01 | 96713 | 10.150804 |
| 2.400000e+02 | 66814 | 7.012664 |
| 1.440000e+02 | 51175 | 5.371226 |
| 7.200000e+01 | 35993 | 3.777754 |
| 3.600000e+02 | 25096 | 2.634026 |
| 3.000000e+02 | 24793 | 2.602224 |
| 3.600000e+01 | 22838 | 2.397031 |
| 9.600000e+01 | 20117 | 2.111440 |
| 8.400000e+01 | 16966 | 1.780718 |
| 1.680000e+02 | 16565 | 1.738629 |
| 1.000000e+02 | 14049 | 1.474555 |
| 7.212000e+01 | 11666 | 1.224440 |
| 5.000000e+01 | 9134 | 0.958686 |
| 2.040000e+02 | 8534 | 0.895712 |
| 4.000000e+01 | 8400 | 0.881647 |
| 8.000000e+01 | 7842 | 0.823081 |
| 6.000000e+02 | 7823 | 0.821086 |
| 2.000000e+02 | 7589 | 0.796526 |
| 2.000000e+01 | 7526 | 0.789914 |
| 4.800000e+02 | 6982 | 0.732817 |
| 2.160000e+02 | 6854 | 0.719382 |
| 3.000000e+01 | 6570 | 0.689574 |
| 1.320000e+02 | 6168 | 0.647381 |
| 1.560000e+02 | 6143 | 0.644757 |
| 1.500000e+02 | 5878 | 0.616943 |
| 1.080000e+02 | 5724 | 0.600780 |
| 1.920000e+02 | 5646 | 0.592593 |
| 4.800000e+01 | 5377 | 0.564359 |
| 4.200000e+02 | 5289 | 0.555123 |
| 3.120000e+02 | 4814 | 0.505268 |
| 2.640000e+02 | 4417 | 0.463600 |
| 1.200000e+01 | 4115 | 0.431902 |
| 5.196000e+01 | 3794 | 0.398211 |
| 6.010000e+01 | 3638 | 0.381837 |
| 1.202000e+02 | 3425 | 0.359481 |
| 2.280000e+02 | 3416 | 0.358537 |
| 1.000000e+01 | 3402 | 0.357067 |
| 1.600000e+02 | 3319 | 0.348356 |
| 7.200000e+02 | 3190 | 0.334816 |
| 3.005000e+01 | 3140 | 0.329568 |
| 2.760000e+02 | 2810 | 0.294932 |
| 9.000000e+01 | 2692 | 0.282547 |
| 1.442400e+02 | 2618 | 0.274780 |
| 1.500000e+01 | 2532 | 0.265754 |
| 1.400000e+02 | 2396 | 0.251479 |
| 2.163600e+02 | 2215 | 0.232482 |
| 7.000000e+01 | 2195 | 0.230383 |
| 3.606000e+02 | 2183 | 0.229123 |
| 4.000000e+02 | 1865 | 0.195747 |
| 3.840000e+02 | 1834 | 0.192493 |
| 1.200000e+03 | 1833 | 0.192388 |
| 2.500000e+01 | 1709 | 0.179373 |
| 5.400000e+02 | 1644 | 0.172551 |
| 2.880000e+02 | 1527 | 0.160271 |
| 7.500000e+01 | 1462 | 0.153449 |
| 2.400000e+01 | 1415 | 0.148516 |
| 2.520000e+02 | 1318 | 0.138335 |
| 3.240000e+02 | 1265 | 0.132772 |
| 2.500000e+02 | 1243 | 0.130463 |
| 3.360000e+02 | 1227 | 0.128783 |
| 3.000000e+00 | 1158 | 0.121541 |
| 1.803000e+01 | 1102 | 0.115664 |
| 2.600000e+02 | 1070 | 0.112305 |
| 9.015000e+01 | 1023 | 0.107372 |
| 2.404000e+02 | 1008 | 0.105798 |
| 3.960000e+02 | 827 | 0.086800 |
| 5.000000e+00 | 812 | 0.085226 |
| 1.300000e+02 | 770 | 0.080818 |
| 5.000000e+02 | 757 | 0.079453 |
| 1.100000e+02 | 752 | 0.078928 |
| 2.800000e+02 | 737 | 0.077354 |
| 2.200000e+02 | 676 | 0.070952 |
| 1.250000e+02 | 657 | 0.068957 |
| 3.500000e+01 | 646 | 0.067803 |
| 8.400000e+02 | 641 | 0.067278 |
| 6.600000e+02 | 641 | 0.067278 |
| 3.200000e+02 | 604 | 0.063395 |
| 4.500000e+01 | 599 | 0.062870 |
| 1.800000e+01 | 516 | 0.054158 |
| 4.808000e+01 | 492 | 0.051639 |
| 7.212000e+02 | 487 | 0.051115 |
| 0.000000e+00 | 465 | 0.048805 |
| 6.500000e+01 | 458 | 0.048071 |
| 4.080000e+02 | 449 | 0.047126 |
| 9.000000e+02 | 445 | 0.046706 |
| 8.800000e+01 | 444 | 0.046601 |
| 4.320000e+02 | 443 | 0.046496 |
| 9.600000e+02 | 436 | 0.045762 |
| 1.700000e+02 | 431 | 0.045237 |
| 3.200000e+01 | 425 | 0.044607 |
| 4.200000e+01 | 397 | 0.041668 |
| 1.502500e+02 | 395 | 0.041458 |
| 2.800000e+01 | 385 | 0.040409 |
| 2.100000e+02 | 373 | 0.039149 |
| 1.000000e+03 | 367 | 0.038520 |
| 5.500000e+01 | 366 | 0.038415 |
| 7.800000e+02 | 361 | 0.037890 |
| 5.200000e+01 | 359 | 0.037680 |
| 5.600000e+01 | 341 | 0.035791 |
| 4.440000e+02 | 340 | 0.035686 |
| 3.500000e+02 | 339 | 0.035581 |
| 2.404000e+01 | 336 | 0.035266 |
| 3.720000e+02 | 328 | 0.034426 |
| 6.240000e+02 | 324 | 0.034006 |
| 5.040000e+02 | 319 | 0.033482 |
| 1.750000e+02 | 316 | 0.033167 |
| 8.000000e+02 | 294 | 0.030858 |
| 3.606000e+01 | 289 | 0.030333 |
| 1.080000e+03 | 282 | 0.029598 |
| 8.500000e+01 | 274 | 0.028758 |
| 1.650000e+02 | 274 | 0.028758 |
| 1.120000e+02 | 268 | 0.028129 |
| 1.081200e+02 | 257 | 0.026974 |
| 2.200000e+01 | 253 | 0.026554 |
| 2.300000e+02 | 245 | 0.025715 |
| 6.000000e+00 | 244 | 0.025610 |
| 3.480000e+02 | 241 | 0.025295 |
| 1.800000e+03 | 239 | 0.025085 |
| 4.560000e+02 | 236 | 0.024770 |
| 5.200000e+02 | 231 | 0.024245 |
| 9.200000e+01 | 231 | 0.024245 |
| 1.040400e+02 | 195 | 0.020467 |
| 1.802400e+02 | 192 | 0.020152 |
| 2.884800e+02 | 192 | 0.020152 |
| 1.050000e+02 | 189 | 0.019837 |
| 6.800000e+01 | 189 | 0.019837 |
| 1.040000e+02 | 174 | 0.018263 |
| 1.520000e+02 | 170 | 0.017843 |
| 6.400000e+01 | 170 | 0.017843 |
| 1.600000e+01 | 167 | 0.017528 |
| 1.400000e+01 | 165 | 0.017318 |
| 3.485000e+01 | 165 | 0.017318 |
| 2.400000e+03 | 163 | 0.017108 |
| 1.280000e+02 | 162 | 0.017003 |
| 9.616000e+01 | 162 | 0.017003 |
| 5.160000e+02 | 157 | 0.016478 |
| 3.400000e+02 | 156 | 0.016373 |
| 4.400000e+02 | 155 | 0.016268 |
| 1.803000e+02 | 152 | 0.015954 |
| 1.350000e+02 | 151 | 0.015849 |
| 1.202000e+01 | 145 | 0.015219 |
| 1.730400e+02 | 142 | 0.014904 |
| 3.486000e+01 | 142 | 0.014904 |
| 1.240000e+02 | 141 | 0.014799 |
| 1.900000e+02 | 140 | 0.014694 |
| 5.280000e+02 | 140 | 0.014694 |
| 5.400000e+01 | 140 | 0.014694 |
| 5.520000e+02 | 139 | 0.014589 |
| 2.240000e+02 | 138 | 0.014484 |
| 1.394000e+02 | 137 | 0.014379 |
| 8.640000e+02 | 135 | 0.014169 |
| 1.440000e+03 | 135 | 0.014169 |
| 1.150000e+02 | 134 | 0.014064 |
| 6.200000e+01 | 134 | 0.014064 |
| 2.250000e+02 | 133 | 0.013959 |
| 6.010000e+00 | 130 | 0.013645 |
| 1.700000e+01 | 124 | 0.013015 |
| 4.400000e+01 | 124 | 0.013015 |
| 4.500000e+02 | 124 | 0.013015 |
| 1.039200e+02 | 122 | 0.012805 |
| 1.500000e+03 | 116 | 0.012175 |
| 1.480000e+02 | 112 | 0.011755 |
| 1.394400e+02 | 111 | 0.011650 |
| 7.224000e+01 | 110 | 0.011545 |
| 2.700000e+02 | 110 | 0.011545 |
| 7.000000e+02 | 110 | 0.011545 |
| 9.500000e+01 | 109 | 0.011440 |
| 8.000000e+00 | 109 | 0.011440 |
| 3.005000e+02 | 108 | 0.011335 |
| 2.320000e+02 | 108 | 0.011335 |
| 6.600000e+01 | 102 | 0.010706 |
| 1.082400e+02 | 102 | 0.010706 |
| 3.800000e+01 | 101 | 0.010601 |
| 4.920000e+02 | 98 | 0.010286 |
| 4.680000e+02 | 97 | 0.010181 |
| 3.612000e+01 | 94 | 0.009866 |
| 2.100000e+01 | 92 | 0.009656 |
| 7.600000e+01 | 91 | 0.009551 |
| 3.800000e+02 | 90 | 0.009446 |
| 1.020000e+03 | 89 | 0.009341 |
| 4.600000e+02 | 87 | 0.009131 |
| 1.360000e+02 | 82 | 0.008607 |
| 1.550000e+02 | 81 | 0.008502 |
| 4.327200e+02 | 80 | 0.008397 |
| 1.840000e+02 | 80 | 0.008397 |
| 5.760000e+02 | 77 | 0.008082 |
| 1.640000e+02 | 76 | 0.007977 |
| 3.604000e+01 | 75 | 0.007872 |
| 1.803600e+02 | 73 | 0.007662 |
| 3.100000e+02 | 73 | 0.007662 |
| 3.300000e+02 | 71 | 0.007452 |
| 3.900000e+02 | 67 | 0.007032 |
| 5.768000e+01 | 67 | 0.007032 |
| 1.081800e+03 | 66 | 0.006927 |
| 2.000000e+03 | 66 | 0.006927 |
| 7.400000e+01 | 65 | 0.006822 |
| 2.600000e+01 | 64 | 0.006717 |
| 9.315000e+01 | 64 | 0.006717 |
| 4.207000e+01 | 63 | 0.006612 |
| 2.750000e+02 | 63 | 0.006612 |
| 1.320000e+03 | 62 | 0.006507 |
| 3.300000e+01 | 59 | 0.006193 |
| 5.600000e+02 | 58 | 0.006088 |
| 1.923200e+02 | 57 | 0.005983 |
| 1.020000e+02 | 57 | 0.005983 |
| 4.808000e+02 | 56 | 0.005878 |
| 7.800000e+01 | 56 | 0.005878 |
| 6.010000e+02 | 55 | 0.005773 |
| 1.260000e+02 | 55 | 0.005773 |
| 2.080000e+02 | 55 | 0.005773 |
| 6.300000e+01 | 54 | 0.005668 |
| 2.480000e+02 | 54 | 0.005668 |
| 6.400000e+02 | 52 | 0.005458 |
| 4.183200e+02 | 52 | 0.005458 |
| 3.600000e+03 | 51 | 0.005353 |
| 1.450000e+02 | 51 | 0.005353 |
| 5.500000e+02 | 51 | 0.005353 |
| 2.700000e+01 | 51 | 0.005353 |
| 6.396000e+01 | 51 | 0.005353 |
| 6.010100e+02 | 51 | 0.005353 |
| 2.120000e+02 | 49 | 0.005143 |
| 3.400000e+01 | 49 | 0.005143 |
| 3.000000e+03 | 48 | 0.005038 |
| 7.440000e+02 | 48 | 0.005038 |
| 6.480000e+02 | 47 | 0.004933 |
| 1.682800e+02 | 47 | 0.004933 |
| 5.640000e+02 | 47 | 0.004933 |
| 1.160000e+02 | 46 | 0.004828 |
| 1.850000e+02 | 45 | 0.004723 |
| 6.200000e+02 | 44 | 0.004618 |
| 9.316000e+01 | 44 | 0.004618 |
| 7.596000e+01 | 44 | 0.004618 |
| 5.769600e+02 | 44 | 0.004618 |
| 8.414000e+01 | 43 | 0.004513 |
| 1.720000e+02 | 43 | 0.004513 |
| 6.008000e+01 | 42 | 0.004408 |
| 5.048400e+02 | 41 | 0.004303 |
| 7.000000e+00 | 41 | 0.004303 |
| 8.200000e+01 | 40 | 0.004198 |
| 1.300000e+01 | 39 | 0.004093 |
| 3.640000e+02 | 39 | 0.004093 |
| 3.700000e+01 | 38 | 0.003988 |
| 2.900000e+02 | 38 | 0.003988 |
| 8.654400e+02 | 37 | 0.003883 |
| 1.620000e+02 | 37 | 0.003883 |
| 1.960000e+02 | 37 | 0.003883 |
| 6.360000e+02 | 36 | 0.003778 |
| 5.409000e+01 | 35 | 0.003674 |
| 7.920000e+02 | 35 | 0.003674 |
| 1.880000e+02 | 35 | 0.003674 |
| 6.120000e+02 | 35 | 0.003674 |
| 3.250000e+02 | 34 | 0.003569 |
| 3.462000e+02 | 33 | 0.003464 |
| 4.000000e+00 | 33 | 0.003464 |
| 3.750000e+02 | 33 | 0.003464 |
| 6.500000e+02 | 32 | 0.003359 |
| 3.005100e+02 | 31 | 0.003254 |
| 4.182000e+02 | 31 | 0.003254 |
| 1.560000e+03 | 31 | 0.003254 |
| 1.081800e+02 | 31 | 0.003254 |
| 1.760000e+02 | 31 | 0.003254 |
| 3.900000e+01 | 31 | 0.003254 |
| 1.442400e+03 | 30 | 0.003149 |
| 1.802800e+02 | 30 | 0.003149 |
| 4.600000e+01 | 29 | 0.003044 |
| 1.008000e+03 | 29 | 0.003044 |
| 8.412000e+01 | 28 | 0.002939 |
| 9.000000e+00 | 27 | 0.002834 |
| 2.720000e+02 | 27 | 0.002834 |
| 3.700000e+02 | 27 | 0.002834 |
| 9.800000e+01 | 27 | 0.002834 |
| 1.154000e+02 | 25 | 0.002624 |
| 1.100000e+01 | 25 | 0.002624 |
| 2.360000e+02 | 25 | 0.002624 |
| 3.650000e+02 | 25 | 0.002624 |
| 3.608000e+01 | 24 | 0.002519 |
| 6.000000e+03 | 24 | 0.002519 |
| 6.700000e+01 | 24 | 0.002519 |
| 9.400000e+01 | 24 | 0.002519 |
| 1.740000e+02 | 23 | 0.002414 |
| 1.442000e+01 | 23 | 0.002414 |
| 2.885000e+01 | 23 | 0.002414 |
| 3.920000e+02 | 22 | 0.002309 |
| 2.350000e+02 | 22 | 0.002309 |
| 2.884000e+01 | 22 | 0.002309 |
| 3.614400e+02 | 22 | 0.002309 |
| 7.700000e+01 | 21 | 0.002204 |
| 2.150000e+02 | 21 | 0.002204 |
| 3.100000e+01 | 20 | 0.002099 |
| 9.360000e+02 | 20 | 0.002099 |
| 1.600000e+03 | 20 | 0.002099 |
| 7.813000e+01 | 20 | 0.002099 |
| 2.300000e+01 | 19 | 0.001994 |
| 7.500000e+02 | 19 | 0.001994 |
| 1.140000e+03 | 19 | 0.001994 |
| 1.803000e+03 | 19 | 0.001994 |
| 7.600000e+02 | 19 | 0.001994 |
| 8.600000e+01 | 19 | 0.001994 |
| 2.523600e+02 | 19 | 0.001994 |
| 6.100000e+01 | 19 | 0.001994 |
| 8.460000e+01 | 18 | 0.001889 |
| 6.720000e+02 | 18 | 0.001889 |
| 6.800000e+02 | 18 | 0.001889 |
| 1.153600e+02 | 17 | 0.001784 |
| 2.440000e+02 | 17 | 0.001784 |
| 5.100000e+01 | 17 | 0.001784 |
| 2.050000e+02 | 16 | 0.001679 |
| 1.140000e+02 | 16 | 0.001679 |
| 7.560000e+02 | 16 | 0.001679 |
| 8.700000e+01 | 16 | 0.001679 |
| 4.250000e+02 | 16 | 0.001679 |
| 6.840000e+02 | 15 | 0.001574 |
| 6.012000e+01 | 15 | 0.001574 |
| 1.152000e+03 | 15 | 0.001574 |
| 3.040000e+02 | 15 | 0.001574 |
| 1.680000e+03 | 15 | 0.001574 |
| 9.012000e+01 | 15 | 0.001574 |
| 1.220000e+02 | 15 | 0.001574 |
| 1.260000e+03 | 15 | 0.001574 |
| 5.300000e+01 | 14 | 0.001469 |
| 5.770000e+01 | 14 | 0.001469 |
| 1.980000e+02 | 14 | 0.001469 |
| 6.960000e+02 | 14 | 0.001469 |
| 5.769000e+01 | 14 | 0.001469 |
| 1.380000e+02 | 14 | 0.001469 |
| 2.103500e+02 | 14 | 0.001469 |
| 7.300000e+01 | 14 | 0.001469 |
| 8.652000e+01 | 14 | 0.001469 |
| 6.024000e+01 | 13 | 0.001364 |
| 7.320000e+02 | 13 | 0.001364 |
| 4.100000e+02 | 13 | 0.001364 |
| 1.060000e+02 | 13 | 0.001364 |
| 2.920000e+02 | 13 | 0.001364 |
| 1.950000e+02 | 13 | 0.001364 |
| 5.700000e+01 | 13 | 0.001364 |
| 1.204800e+02 | 13 | 0.001364 |
| 1.032000e+03 | 12 | 0.001259 |
| 1.201200e+02 | 12 | 0.001259 |
| 3.726000e+02 | 12 | 0.001259 |
| 4.300000e+01 | 12 | 0.001259 |
| 1.200000e+04 | 12 | 0.001259 |
| 7.680000e+02 | 12 | 0.001259 |
| 5.800000e+02 | 12 | 0.001259 |
| 1.340000e+02 | 12 | 0.001259 |
| 2.680000e+02 | 12 | 0.001259 |
| 9.300000e+01 | 12 | 0.001259 |
| 1.540000e+02 | 11 | 0.001155 |
| 8.040000e+02 | 11 | 0.001155 |
| 5.800000e+01 | 11 | 0.001155 |
| 2.524800e+02 | 11 | 0.001155 |
| 4.100000e+01 | 11 | 0.001155 |
| 2.840000e+02 | 11 | 0.001155 |
| 3.440000e+02 | 11 | 0.001155 |
| 1.230000e+02 | 11 | 0.001155 |
| 1.400000e+03 | 11 | 0.001155 |
| 1.803200e+02 | 11 | 0.001155 |
| 1.094400e+02 | 11 | 0.001155 |
| 1.420000e+02 | 11 | 0.001155 |
| 2.160000e+03 | 10 | 0.001050 |
| 8.800000e+02 | 10 | 0.001050 |
| 3.460800e+02 | 10 | 0.001050 |
| 1.010000e+02 | 10 | 0.001050 |
| 2.550000e+02 | 10 | 0.001050 |
| 4.800000e+03 | 10 | 0.001050 |
| 4.700000e+01 | 10 | 0.001050 |
| 1.202000e+03 | 9 | 0.000945 |
| 9.996000e+01 | 9 | 0.000945 |
| 2.040000e+03 | 9 | 0.000945 |
| 1.100000e+03 | 9 | 0.000945 |
| 3.160000e+02 | 9 | 0.000945 |
| 1.081600e+02 | 9 | 0.000945 |
| 6.924000e+02 | 9 | 0.000945 |
| 1.719600e+02 | 9 | 0.000945 |
| 1.920000e+03 | 9 | 0.000945 |
| 1.900000e+01 | 9 | 0.000945 |
| 2.020000e+02 | 9 | 0.000945 |
| 4.700000e+02 | 9 | 0.000945 |
| 2.560000e+02 | 9 | 0.000945 |
| 1.380000e+03 | 9 | 0.000945 |
| 1.040000e+03 | 8 | 0.000840 |
| 8.300000e+01 | 8 | 0.000840 |
| 2.220000e+02 | 8 | 0.000840 |
| 1.824000e+02 | 8 | 0.000840 |
| 2.900000e+01 | 8 | 0.000840 |
| 8.100000e+01 | 8 | 0.000840 |
| 9.020000e+00 | 8 | 0.000840 |
| 6.611000e+01 | 8 | 0.000840 |
| 3.012000e+01 | 8 | 0.000840 |
| 3.320000e+02 | 8 | 0.000840 |
| 2.960000e+02 | 8 | 0.000840 |
| 3.280000e+02 | 8 | 0.000840 |
| 1.820000e+02 | 8 | 0.000840 |
| 8.416000e+01 | 7 | 0.000735 |
| 8.160000e+02 | 7 | 0.000735 |
| 9.612000e+01 | 7 | 0.000735 |
| 2.850000e+02 | 7 | 0.000735 |
| 1.180000e+02 | 7 | 0.000735 |
| 2.163600e+03 | 7 | 0.000735 |
| 4.300000e+02 | 7 | 0.000735 |
| 4.900000e+01 | 7 | 0.000735 |
| 2.160000e+01 | 7 | 0.000735 |
| 7.200000e+03 | 7 | 0.000735 |
| 5.880000e+02 | 7 | 0.000735 |
| 1.860000e+02 | 7 | 0.000735 |
| 9.200000e+02 | 7 | 0.000735 |
| 5.040000e+01 | 7 | 0.000735 |
| 3.760000e+02 | 7 | 0.000735 |
| 2.100000e+03 | 7 | 0.000735 |
| 8.200000e+02 | 7 | 0.000735 |
| 1.082000e+02 | 7 | 0.000735 |
| 5.052000e+01 | 6 | 0.000630 |
| 3.050000e+02 | 6 | 0.000630 |
| 1.204000e+01 | 6 | 0.000630 |
| 3.004800e+02 | 6 | 0.000630 |
| 9.840000e+02 | 6 | 0.000630 |
| 1.503000e+01 | 6 | 0.000630 |
| 9.100000e+01 | 6 | 0.000630 |
| 8.520000e+02 | 6 | 0.000630 |
| 3.726400e+02 | 6 | 0.000630 |
| 3.080000e+02 | 6 | 0.000630 |
| 4.480000e+02 | 6 | 0.000630 |
| 4.507000e+01 | 6 | 0.000630 |
| 3.606000e+03 | 6 | 0.000630 |
| 9.036000e+01 | 6 | 0.000630 |
| 2.404100e+02 | 6 | 0.000630 |
| 8.500000e+02 | 6 | 0.000630 |
| 8.880000e+02 | 6 | 0.000630 |
| 3.606120e+03 | 5 | 0.000525 |
| 3.010000e+00 | 5 | 0.000525 |
| 1.322000e+02 | 5 | 0.000525 |
| 4.880000e+02 | 5 | 0.000525 |
| 2.403600e+02 | 5 | 0.000525 |
| 1.440000e+01 | 5 | 0.000525 |
| 1.620000e+03 | 5 | 0.000525 |
| 2.450000e+02 | 5 | 0.000525 |
| 6.490800e+02 | 5 | 0.000525 |
| 4.160000e+02 | 5 | 0.000525 |
| 1.210000e+02 | 5 | 0.000525 |
| 9.700000e+01 | 5 | 0.000525 |
| 1.250000e+03 | 5 | 0.000525 |
| 8.796000e+01 | 5 | 0.000525 |
| 7.400000e+02 | 5 | 0.000525 |
| 9.010000e+00 | 5 | 0.000525 |
| 3.680000e+02 | 5 | 0.000525 |
| 9.240000e+02 | 5 | 0.000525 |
| 1.120000e+03 | 5 | 0.000525 |
| 3.880000e+02 | 4 | 0.000420 |
| 2.010000e+02 | 4 | 0.000420 |
| 5.000000e+03 | 4 | 0.000420 |
| 1.610000e+02 | 4 | 0.000420 |
| 8.760000e+02 | 4 | 0.000420 |
| 3.520000e+02 | 4 | 0.000420 |
| 4.320000e+01 | 4 | 0.000420 |
| 4.000000e+03 | 4 | 0.000420 |
| 2.740000e+02 | 4 | 0.000420 |
| 4.750000e+02 | 4 | 0.000420 |
| 1.030000e+02 | 4 | 0.000420 |
| 2.650000e+02 | 4 | 0.000420 |
| 9.120000e+02 | 4 | 0.000420 |
| 2.340000e+02 | 4 | 0.000420 |
| 1.001500e+02 | 4 | 0.000420 |
| 3.900000e+03 | 4 | 0.000420 |
| 1.021700e+02 | 4 | 0.000420 |
| 4.240000e+02 | 4 | 0.000420 |
| 9.960000e+02 | 4 | 0.000420 |
| 1.660000e+02 | 4 | 0.000420 |
| 1.870000e+02 | 4 | 0.000420 |
| 1.502000e+01 | 4 | 0.000420 |
| 3.606100e+02 | 4 | 0.000420 |
| 1.202040e+03 | 4 | 0.000420 |
| 6.250000e+02 | 4 | 0.000420 |
| 1.800000e+04 | 4 | 0.000420 |
| 7.200000e+00 | 4 | 0.000420 |
| 3.150000e+02 | 4 | 0.000420 |
| 9.900000e+01 | 4 | 0.000420 |
| 4.005000e+01 | 4 | 0.000420 |
| 5.408400e+02 | 4 | 0.000420 |
| 1.490000e+02 | 4 | 0.000420 |
| 2.860000e+02 | 4 | 0.000420 |
| 3.450000e+02 | 4 | 0.000420 |
| 7.210000e+00 | 4 | 0.000420 |
| 7.080000e+02 | 4 | 0.000420 |
| 1.460000e+02 | 4 | 0.000420 |
| 7.932000e+01 | 4 | 0.000420 |
| 1.940000e+02 | 4 | 0.000420 |
| 4.150000e+02 | 4 | 0.000420 |
| 4.507500e+02 | 4 | 0.000420 |
| 3.004000e+01 | 4 | 0.000420 |
| 1.370000e+02 | 3 | 0.000315 |
| 9.999600e+02 | 3 | 0.000315 |
| 4.120000e+02 | 3 | 0.000315 |
| 7.100000e+01 | 3 | 0.000315 |
| 1.510000e+02 | 3 | 0.000315 |
| 7.228800e+02 | 3 | 0.000315 |
| 1.128000e+03 | 3 | 0.000315 |
| 1.300000e+03 | 3 | 0.000315 |
| 2.880000e+03 | 3 | 0.000315 |
| 7.212100e+02 | 3 | 0.000315 |
| 6.922800e+02 | 3 | 0.000315 |
| 4.806000e+02 | 3 | 0.000315 |
| 2.620000e+02 | 3 | 0.000315 |
| 1.130000e+02 | 3 | 0.000315 |
| 1.464000e+03 | 3 | 0.000315 |
| 7.010000e+01 | 3 | 0.000315 |
| 1.890000e+02 | 3 | 0.000315 |
| 3.200000e+03 | 3 | 0.000315 |
| 6.510000e+01 | 3 | 0.000315 |
| 8.280000e+02 | 3 | 0.000315 |
| 6.005000e+01 | 3 | 0.000315 |
| 1.580000e+02 | 3 | 0.000315 |
| 1.280000e+03 | 3 | 0.000315 |
| 4.200000e+03 | 3 | 0.000315 |
| 9.720000e+02 | 3 | 0.000315 |
| 2.409600e+02 | 3 | 0.000315 |
| 8.414000e+02 | 3 | 0.000315 |
| 1.224000e+03 | 3 | 0.000315 |
| 8.900000e+01 | 3 | 0.000315 |
| 7.250000e+02 | 3 | 0.000315 |
| 1.482400e+02 | 3 | 0.000315 |
| 3.505000e+01 | 3 | 0.000315 |
| 1.732000e+01 | 3 | 0.000315 |
| 1.284000e+03 | 3 | 0.000315 |
| 3.560000e+02 | 3 | 0.000315 |
| 3.365600e+02 | 3 | 0.000315 |
| 5.100000e+02 | 3 | 0.000315 |
| 4.687200e+02 | 3 | 0.000315 |
| 9.372000e+01 | 3 | 0.000315 |
| 1.730000e+02 | 3 | 0.000315 |
| 9.375600e+02 | 3 | 0.000315 |
| 4.206000e+02 | 3 | 0.000315 |
| 8.600000e+02 | 3 | 0.000315 |
| 3.330000e+02 | 3 | 0.000315 |
| 3.966000e+02 | 3 | 0.000315 |
| 1.599600e+02 | 3 | 0.000315 |
| 2.644400e+02 | 3 | 0.000315 |
| 7.212120e+03 | 3 | 0.000315 |
| 3.060000e+02 | 3 | 0.000315 |
| 1.000000e+00 | 3 | 0.000315 |
| 5.760000e+01 | 3 | 0.000315 |
| 9.212000e+01 | 3 | 0.000315 |
| 4.330000e+00 | 2 | 0.000210 |
| 7.200000e+04 | 2 | 0.000210 |
| 2.239200e+02 | 2 | 0.000210 |
| 1.570000e+02 | 2 | 0.000210 |
| 7.992000e+01 | 2 | 0.000210 |
| 1.530000e+02 | 2 | 0.000210 |
| 3.605000e+01 | 2 | 0.000210 |
| 5.046000e+02 | 2 | 0.000210 |
| 1.262100e+02 | 2 | 0.000210 |
| 2.380000e+02 | 2 | 0.000210 |
| 5.592000e+01 | 2 | 0.000210 |
| 1.999200e+02 | 2 | 0.000210 |
| 3.005200e+02 | 2 | 0.000210 |
| 2.400000e+04 | 2 | 0.000210 |
| 4.460000e+02 | 2 | 0.000210 |
| 3.460000e+02 | 2 | 0.000210 |
| 4.360000e+02 | 2 | 0.000210 |
| 5.409600e+02 | 2 | 0.000210 |
| 7.612000e+01 | 2 | 0.000210 |
| 9.012000e+02 | 2 | 0.000210 |
| 1.872000e+02 | 2 | 0.000210 |
| 2.500000e+03 | 2 | 0.000210 |
| 5.360000e+02 | 2 | 0.000210 |
| 3.666000e+01 | 2 | 0.000210 |
| 2.000400e+02 | 2 | 0.000210 |
| 1.070000e+02 | 2 | 0.000210 |
| 1.360000e+03 | 2 | 0.000210 |
| 1.110000e+02 | 2 | 0.000210 |
| 1.980000e+03 | 2 | 0.000210 |
| 1.090000e+02 | 2 | 0.000210 |
| 1.594000e+02 | 2 | 0.000210 |
| 5.700000e+02 | 2 | 0.000210 |
| 1.562400e+02 | 2 | 0.000210 |
| 2.180000e+02 | 2 | 0.000210 |
| 1.282000e+02 | 2 | 0.000210 |
| 4.320000e+03 | 2 | 0.000210 |
| 1.009200e+02 | 2 | 0.000210 |
| 9.015200e+02 | 2 | 0.000210 |
| 5.900000e+01 | 2 | 0.000210 |
| 2.140000e+02 | 2 | 0.000210 |
| 1.602000e+02 | 2 | 0.000210 |
| 1.670000e+02 | 2 | 0.000210 |
| 1.225200e+02 | 2 | 0.000210 |
| 1.056000e+03 | 2 | 0.000210 |
| 2.880000e+01 | 2 | 0.000210 |
| 2.763600e+02 | 2 | 0.000210 |
| 5.999000e+01 | 2 | 0.000210 |
| 8.100000e+02 | 2 | 0.000210 |
| 1.270000e+02 | 2 | 0.000210 |
| 5.202000e+01 | 2 | 0.000210 |
| 2.406000e+02 | 2 | 0.000210 |
| 2.307600e+02 | 2 | 0.000210 |
| 3.020000e+02 | 2 | 0.000210 |
| 4.360000e+03 | 2 | 0.000210 |
| 9.496000e+01 | 2 | 0.000210 |
| 1.470000e+02 | 2 | 0.000210 |
| 5.406000e+02 | 2 | 0.000210 |
| 3.660000e+02 | 2 | 0.000210 |
| 1.202020e+03 | 2 | 0.000210 |
| 1.658400e+02 | 2 | 0.000210 |
| 1.212000e+03 | 2 | 0.000210 |
| 6.900000e+01 | 2 | 0.000210 |
| 9.324000e+01 | 2 | 0.000210 |
| 3.885000e+01 | 2 | 0.000210 |
| 2.820000e+02 | 2 | 0.000210 |
| 1.081840e+03 | 2 | 0.000210 |
| 3.180000e+02 | 2 | 0.000210 |
| 2.940000e+02 | 2 | 0.000210 |
| 1.982400e+02 | 2 | 0.000210 |
| 1.596000e+03 | 2 | 0.000210 |
| 2.950000e+02 | 2 | 0.000210 |
| 1.586400e+02 | 2 | 0.000210 |
| 8.660000e+00 | 2 | 0.000210 |
| 1.983300e+02 | 2 | 0.000210 |
| 5.300000e+02 | 2 | 0.000210 |
| 5.850000e+02 | 2 | 0.000210 |
| 2.499600e+02 | 2 | 0.000210 |
| 2.704000e+01 | 2 | 0.000210 |
| 1.092000e+03 | 2 | 0.000210 |
| 4.508000e+01 | 2 | 0.000210 |
| 7.572000e+01 | 2 | 0.000210 |
| 5.250000e+02 | 2 | 0.000210 |
| 1.322200e+02 | 2 | 0.000210 |
| 3.620000e+02 | 2 | 0.000210 |
| 3.996000e+01 | 2 | 0.000210 |
| 1.200000e+00 | 2 | 0.000210 |
| 9.960000e+01 | 2 | 0.000210 |
| 5.289000e+01 | 2 | 0.000210 |
| 1.104000e+03 | 2 | 0.000210 |
| 2.700000e+03 | 2 | 0.000210 |
| 2.200000e+03 | 2 | 0.000210 |
| 1.770000e+02 | 2 | 0.000210 |
| 7.212200e+02 | 2 | 0.000210 |
| 7.900000e+01 | 2 | 0.000210 |
| 1.170000e+02 | 2 | 0.000210 |
| 1.250000e+01 | 2 | 0.000210 |
| 1.060000e+03 | 2 | 0.000210 |
| 3.350000e+02 | 2 | 0.000210 |
| 6.242400e+02 | 2 | 0.000210 |
| 8.640000e+01 | 2 | 0.000210 |
| 4.205000e+01 | 2 | 0.000210 |
| 2.379600e+02 | 2 | 0.000210 |
| 2.043200e+02 | 2 | 0.000210 |
| 1.860000e+03 | 2 | 0.000210 |
| 2.103000e+01 | 2 | 0.000210 |
| 1.321200e+02 | 1 | 0.000105 |
| 1.644000e+03 | 1 | 0.000105 |
| 3.901500e+02 | 1 | 0.000105 |
| 2.804000e+02 | 1 | 0.000105 |
| 1.830000e+02 | 1 | 0.000105 |
| 6.060000e+01 | 1 | 0.000105 |
| 1.001000e+02 | 1 | 0.000105 |
| 1.050000e+01 | 1 | 0.000105 |
| 8.166000e+03 | 1 | 0.000105 |
| 5.772000e+01 | 1 | 0.000105 |
| 2.340000e+03 | 1 | 0.000105 |
| 1.602500e+02 | 1 | 0.000105 |
| 7.812000e+02 | 1 | 0.000105 |
| 9.800000e+02 | 1 | 0.000105 |
| 7.100000e+02 | 1 | 0.000105 |
| 1.393600e+02 | 1 | 0.000105 |
| 2.199720e+03 | 1 | 0.000105 |
| 1.803030e+03 | 1 | 0.000105 |
| 1.880400e+02 | 1 | 0.000105 |
| 9.840000e+03 | 1 | 0.000105 |
| 6.360000e+01 | 1 | 0.000105 |
| 8.120000e+02 | 1 | 0.000105 |
| 8.976000e+01 | 1 | 0.000105 |
| 2.004000e+03 | 1 | 0.000105 |
| 1.734000e+01 | 1 | 0.000105 |
| 8.016000e+01 | 1 | 0.000105 |
| 1.975200e+02 | 1 | 0.000105 |
| 5.870000e+00 | 1 | 0.000105 |
| 2.919600e+02 | 1 | 0.000105 |
| 6.750000e+02 | 1 | 0.000105 |
| 2.928000e+02 | 1 | 0.000105 |
| 2.520000e+03 | 1 | 0.000105 |
| 3.648000e+02 | 1 | 0.000105 |
| 6.610000e+01 | 1 | 0.000105 |
| 1.226400e+02 | 1 | 0.000105 |
| 1.220400e+02 | 1 | 0.000105 |
| 3.220000e+02 | 1 | 0.000105 |
| 2.167200e+02 | 1 | 0.000105 |
| 2.760000e+03 | 1 | 0.000105 |
| 3.768000e+02 | 1 | 0.000105 |
| 4.802400e+02 | 1 | 0.000105 |
| 3.439200e+02 | 1 | 0.000105 |
| 4.692000e+01 | 1 | 0.000105 |
| 8.652000e+02 | 1 | 0.000105 |
| 1.200100e+02 | 1 | 0.000105 |
| 1.033720e+03 | 1 | 0.000105 |
| 1.298160e+03 | 1 | 0.000105 |
| 3.846400e+02 | 1 | 0.000105 |
| 8.040000e+01 | 1 | 0.000105 |
| 1.692000e+03 | 1 | 0.000105 |
| 2.016000e+03 | 1 | 0.000105 |
| 1.644000e+02 | 1 | 0.000105 |
| 1.801000e+02 | 1 | 0.000105 |
| 3.360000e+01 | 1 | 0.000105 |
| 5.460000e+01 | 1 | 0.000105 |
| 3.000000e+05 | 1 | 0.000105 |
| 3.040000e+01 | 1 | 0.000105 |
| 2.253800e+03 | 1 | 0.000105 |
| 2.439600e+02 | 1 | 0.000105 |
| 6.720000e+01 | 1 | 0.000105 |
| 2.060000e+02 | 1 | 0.000105 |
| 1.242000e+02 | 1 | 0.000105 |
| 1.402000e+02 | 1 | 0.000105 |
| 1.390000e+02 | 1 | 0.000105 |
| 6.235200e+02 | 1 | 0.000105 |
| 3.996000e+02 | 1 | 0.000105 |
| 1.310000e+02 | 1 | 0.000105 |
| 3.030000e+02 | 1 | 0.000105 |
| 7.960000e+02 | 1 | 0.000105 |
| 6.613200e+02 | 1 | 0.000105 |
| 1.665600e+02 | 1 | 0.000105 |
| 7.920000e+01 | 1 | 0.000105 |
| 2.796000e+02 | 1 | 0.000105 |
| 1.356000e+03 | 1 | 0.000105 |
| 6.696000e+02 | 1 | 0.000105 |
| 5.720000e+02 | 1 | 0.000105 |
| 8.245600e+02 | 1 | 0.000105 |
| 3.800000e+03 | 1 | 0.000105 |
| 2.476800e+02 | 1 | 0.000105 |
| 2.560000e+01 | 1 | 0.000105 |
| 1.201000e+02 | 1 | 0.000105 |
| 2.388000e+02 | 1 | 0.000105 |
| 9.014000e+01 | 1 | 0.000105 |
| 9.732000e+02 | 1 | 0.000105 |
| 3.380000e+02 | 1 | 0.000105 |
| 5.920000e+02 | 1 | 0.000105 |
| 2.811600e+02 | 1 | 0.000105 |
| 3.125200e+02 | 1 | 0.000105 |
| 2.601000e+02 | 1 | 0.000105 |
| 2.580000e+02 | 1 | 0.000105 |
| 1.117800e+03 | 1 | 0.000105 |
| 1.622400e+02 | 1 | 0.000105 |
| 1.802000e+01 | 1 | 0.000105 |
| 9.600000e+00 | 1 | 0.000105 |
| 4.660800e+02 | 1 | 0.000105 |
| 4.086000e+01 | 1 | 0.000105 |
| 1.381500e+02 | 1 | 0.000105 |
| 1.322000e+01 | 1 | 0.000105 |
| 3.840000e+01 | 1 | 0.000105 |
| 4.620000e+02 | 1 | 0.000105 |
| 2.164000e+01 | 1 | 0.000105 |
| 2.404400e+02 | 1 | 0.000105 |
| 2.721200e+02 | 1 | 0.000105 |
| 2.640000e+03 | 1 | 0.000105 |
| 3.028800e+02 | 1 | 0.000105 |
| 6.015000e+01 | 1 | 0.000105 |
| 5.580000e+02 | 1 | 0.000105 |
| 1.804000e+01 | 1 | 0.000105 |
| 2.018400e+02 | 1 | 0.000105 |
| 1.221200e+02 | 1 | 0.000105 |
| 3.110000e+02 | 1 | 0.000105 |
| 5.196000e+02 | 1 | 0.000105 |
| 3.020000e+03 | 1 | 0.000105 |
| 9.999000e+01 | 1 | 0.000105 |
| 1.141800e+02 | 1 | 0.000105 |
| 3.740000e+02 | 1 | 0.000105 |
| 1.410000e+02 | 1 | 0.000105 |
| 3.230000e+02 | 1 | 0.000105 |
| 1.240800e+02 | 1 | 0.000105 |
| 4.080000e+03 | 1 | 0.000105 |
| 9.680000e+02 | 1 | 0.000105 |
| 5.908000e+01 | 1 | 0.000105 |
| 5.160000e+01 | 1 | 0.000105 |
| 3.003600e+02 | 1 | 0.000105 |
| 3.363600e+02 | 1 | 0.000105 |
| 2.080800e+02 | 1 | 0.000105 |
| 1.430000e+02 | 1 | 0.000105 |
| 3.750000e+01 | 1 | 0.000105 |
| 2.884900e+02 | 1 | 0.000105 |
| 7.788000e+02 | 1 | 0.000105 |
| 2.260000e+02 | 1 | 0.000105 |
| 1.892400e+02 | 1 | 0.000105 |
| 5.408000e+01 | 1 | 0.000105 |
| 1.402000e+01 | 1 | 0.000105 |
| 2.800000e+03 | 1 | 0.000105 |
| 2.242400e+02 | 1 | 0.000105 |
| 1.382300e+02 | 1 | 0.000105 |
| 4.928400e+02 | 1 | 0.000105 |
| 2.524000e+02 | 1 | 0.000105 |
| 4.808100e+02 | 1 | 0.000105 |
| 1.009680e+03 | 1 | 0.000105 |
| 5.560000e+02 | 1 | 0.000105 |
| 9.810000e+01 | 1 | 0.000105 |
| 1.501500e+02 | 1 | 0.000105 |
| 1.658800e+02 | 1 | 0.000105 |
| 3.850000e+02 | 1 | 0.000105 |
| 4.500000e+03 | 1 | 0.000105 |
| 6.040000e+02 | 1 | 0.000105 |
| 5.507500e+02 | 1 | 0.000105 |
| 1.522400e+02 | 1 | 0.000105 |
| 9.096000e+01 | 1 | 0.000105 |
| 1.002000e+02 | 1 | 0.000105 |
| 9.015100e+02 | 1 | 0.000105 |
| 4.189200e+02 | 1 | 0.000105 |
| 1.215000e+03 | 1 | 0.000105 |
| 1.027200e+02 | 1 | 0.000105 |
| 7.320000e+01 | 1 | 0.000105 |
| 4.840000e+02 | 1 | 0.000105 |
| 1.512000e+03 | 1 | 0.000105 |
| 1.268400e+02 | 1 | 0.000105 |
| 1.297200e+02 | 1 | 0.000105 |
| 1.110000e+03 | 1 | 0.000105 |
| 2.804000e+01 | 1 | 0.000105 |
| 2.253500e+02 | 1 | 0.000105 |
| 3.002000e+02 | 1 | 0.000105 |
| 3.334800e+02 | 1 | 0.000105 |
| 7.980000e+02 | 1 | 0.000105 |
| 2.810000e+02 | 1 | 0.000105 |
| 9.016000e+01 | 1 | 0.000105 |
| 2.884800e+03 | 1 | 0.000105 |
| 1.002000e+03 | 1 | 0.000105 |
| 2.115200e+02 | 1 | 0.000105 |
| 7.680000e+01 | 1 | 0.000105 |
| 9.075000e+01 | 1 | 0.000105 |
| 2.642400e+02 | 1 | 0.000105 |
| 3.005060e+04 | 1 | 0.000105 |
| 1.520000e+03 | 1 | 0.000105 |
| 7.513000e+01 | 1 | 0.000105 |
| 3.244800e+02 | 1 | 0.000105 |
| 4.094400e+02 | 1 | 0.000105 |
| 1.051000e+02 | 1 | 0.000105 |
| 8.240000e+02 | 1 | 0.000105 |
| 3.050000e+01 | 1 | 0.000105 |
| 1.634000e+02 | 1 | 0.000105 |
| 2.604000e+02 | 1 | 0.000105 |
| 7.001000e+01 | 1 | 0.000105 |
| 2.402000e+02 | 1 | 0.000105 |
| 1.471200e+02 | 1 | 0.000105 |
| 1.200400e+02 | 1 | 0.000105 |
| 6.001000e+01 | 1 | 0.000105 |
| 1.350000e+03 | 1 | 0.000105 |
| 4.207100e+02 | 1 | 0.000105 |
| 1.252000e+02 | 1 | 0.000105 |
| 5.050000e+02 | 1 | 0.000105 |
| 7.813200e+02 | 1 | 0.000105 |
| 3.604800e+02 | 1 | 0.000105 |
| 8.212000e+01 | 1 | 0.000105 |
| 1.485000e+03 | 1 | 0.000105 |
| 7.620000e+01 | 1 | 0.000105 |
| 5.528000e+01 | 1 | 0.000105 |
| 1.680000e+01 | 1 | 0.000105 |
| 1.806000e+01 | 1 | 0.000105 |
| 9.080000e+00 | 1 | 0.000105 |
| 4.327000e+01 | 1 | 0.000105 |
| 2.668800e+02 | 1 | 0.000105 |
| 1.442000e+02 | 1 | 0.000105 |
| 1.250100e+02 | 1 | 0.000105 |
| 2.632000e+01 | 1 | 0.000105 |
| 9.000000e+03 | 1 | 0.000105 |
| 7.933200e+02 | 1 | 0.000105 |
| 4.020000e+02 | 1 | 0.000105 |
| 1.502530e+03 | 1 | 0.000105 |
| 1.080000e+06 | 1 | 0.000105 |
| 1.239600e+02 | 1 | 0.000105 |
| 3.907000e+01 | 1 | 0.000105 |
| 4.399200e+02 | 1 | 0.000105 |
| 7.280000e+02 | 1 | 0.000105 |
| 5.320000e+02 | 1 | 0.000105 |
| 2.472000e+02 | 1 | 0.000105 |
| 1.253300e+02 | 1 | 0.000105 |
| 1.752000e+03 | 1 | 0.000105 |
| 2.058000e+04 | 1 | 0.000105 |
| 3.400800e+02 | 1 | 0.000105 |
| 1.164000e+06 | 1 | 0.000105 |
| 2.700500e+02 | 1 | 0.000105 |
| 2.730000e+02 | 1 | 0.000105 |
| 5.400000e+03 | 1 | 0.000105 |
| 5.949600e+02 | 1 | 0.000105 |
| 1.104000e+02 | 1 | 0.000105 |
| 3.820800e+02 | 1 | 0.000105 |
| 1.600800e+02 | 1 | 0.000105 |
| 1.141900e+02 | 1 | 0.000105 |
| 1.690000e+02 | 1 | 0.000105 |
| 1.382000e+01 | 1 | 0.000105 |
| 2.500000e+04 | 1 | 0.000105 |
| 4.380000e+02 | 1 | 0.000105 |
| 1.642400e+02 | 1 | 0.000105 |
| 2.328000e+03 | 1 | 0.000105 |
| 6.972000e+01 | 1 | 0.000105 |
| 4.020000e+01 | 1 | 0.000105 |
| 1.441200e+02 | 1 | 0.000105 |
| 7.220000e+00 | 1 | 0.000105 |
| 7.710000e+01 | 1 | 0.000105 |
| 1.280100e+02 | 1 | 0.000105 |
| 2.956920e+03 | 1 | 0.000105 |
| 7.230000e+01 | 1 | 0.000105 |
| 1.990000e+02 | 1 | 0.000105 |
| 1.950000e+01 | 1 | 0.000105 |
| 1.750000e+03 | 1 | 0.000105 |
| 5.750000e+02 | 1 | 0.000105 |
| 4.580000e+02 | 1 | 0.000105 |
| 1.200000e+05 | 1 | 0.000105 |
| 4.688400e+02 | 1 | 0.000105 |
| 1.536000e+03 | 1 | 0.000105 |
| 2.704500e+02 | 1 | 0.000105 |
| 1.261500e+02 | 1 | 0.000105 |
| 1.117920e+03 | 1 | 0.000105 |
| 1.502400e+02 | 1 | 0.000105 |
| 2.103200e+02 | 1 | 0.000105 |
| 1.081200e+03 | 1 | 0.000105 |
| 3.180000e+03 | 1 | 0.000105 |
| 4.352400e+02 | 1 | 0.000105 |
| 2.003000e+01 | 1 | 0.000105 |
| 3.065100e+02 | 1 | 0.000105 |
| 4.681200e+02 | 1 | 0.000105 |
| 7.410000e+02 | 1 | 0.000105 |
| 1.762400e+02 | 1 | 0.000105 |
| 1.710000e+02 | 1 | 0.000105 |
| 5.944800e+02 | 1 | 0.000105 |
| 1.992000e+03 | 1 | 0.000105 |
| 9.060000e+02 | 1 | 0.000105 |
| 4.510000e+02 | 1 | 0.000105 |
| 6.490000e+01 | 1 | 0.000105 |
| 3.780000e+02 | 1 | 0.000105 |
| 7.208000e+02 | 1 | 0.000105 |
| 3.588000e+01 | 1 | 0.000105 |
| 1.502400e+03 | 1 | 0.000105 |
| 1.330000e+02 | 1 | 0.000105 |
| 2.043600e+02 | 1 | 0.000105 |
| 1.333200e+02 | 1 | 0.000105 |
| 8.656000e+01 | 1 | 0.000105 |
| 5.120000e+02 | 1 | 0.000105 |
| 1.513200e+02 | 1 | 0.000105 |
| 4.050000e+02 | 1 | 0.000105 |
| 2.127600e+02 | 1 | 0.000105 |
| 1.443000e+02 | 1 | 0.000105 |
| 7.512700e+02 | 1 | 0.000105 |
| 6.130800e+02 | 1 | 0.000105 |
| 3.305000e+01 | 1 | 0.000105 |
| 9.010000e+01 | 1 | 0.000105 |
| 1.800000e+00 | 1 | 0.000105 |
| 1.226040e+03 | 1 | 0.000105 |
| 2.780000e+02 | 1 | 0.000105 |
| 6.080000e+02 | 1 | 0.000105 |
| 9.616000e+02 | 1 | 0.000105 |
| 5.902000e+01 | 1 | 0.000105 |
| 2.619600e+02 | 1 | 0.000105 |
| 3.205000e+02 | 1 | 0.000105 |
| 6.410000e+02 | 1 | 0.000105 |
| 6.910000e+01 | 1 | 0.000105 |
| 2.700300e+02 | 1 | 0.000105 |
| 2.401200e+02 | 1 | 0.000105 |
| 9.010000e+02 | 1 | 0.000105 |
| 1.622700e+02 | 1 | 0.000105 |
| 1.203000e+03 | 1 | 0.000105 |
| 2.901000e+02 | 1 | 0.000105 |
| 1.682400e+02 | 1 | 0.000105 |
| 2.090000e+02 | 1 | 0.000105 |
| 3.205000e+01 | 1 | 0.000105 |
| 6.300000e+02 | 1 | 0.000105 |
| 1.003200e+02 | 1 | 0.000105 |
| 2.280000e+03 | 1 | 0.000105 |
| 8.000000e+03 | 1 | 0.000105 |
| 5.440000e+02 | 1 | 0.000105 |
| 1.008000e+02 | 1 | 0.000105 |
| 1.584000e+03 | 1 | 0.000105 |
| 2.184000e+03 | 1 | 0.000105 |
| 1.116000e+03 | 1 | 0.000105 |
| 4.640000e+02 | 1 | 0.000105 |
| 2.524200e+02 | 1 | 0.000105 |
| 5.650000e+02 | 1 | 0.000105 |
| 3.480000e+03 | 1 | 0.000105 |
| 4.208000e+01 | 1 | 0.000105 |
| 7.995600e+02 | 1 | 0.000105 |
| 6.586000e+01 | 1 | 0.000105 |
| 3.612000e+03 | 1 | 0.000105 |
| 7.927200e+02 | 1 | 0.000105 |
| 2.770000e+02 | 1 | 0.000105 |
| 1.436400e+02 | 1 | 0.000105 |
| 7.809566e+09 | 1 | 0.000105 |
| 3.000000e+04 | 1 | 0.000105 |
| 1.159200e+02 | 1 | 0.000105 |
| 1.992000e+02 | 1 | 0.000105 |
| 3.300000e+03 | 1 | 0.000105 |
| 7.200000e-01 | 1 | 0.000105 |
| 3.889200e+02 | 1 | 0.000105 |
| 1.344000e+02 | 1 | 0.000105 |
| 1.834800e+02 | 1 | 0.000105 |
| 1.019600e+02 | 1 | 0.000105 |
| 1.359600e+02 | 1 | 0.000105 |
| 1.008000e+04 | 1 | 0.000105 |
| 7.640000e+02 | 1 | 0.000105 |
| 4.201000e+03 | 1 | 0.000105 |
| 2.070000e+02 | 1 | 0.000105 |
| 2.592000e+03 | 1 | 0.000105 |
| 1.044000e+03 | 1 | 0.000105 |
| 2.425000e+02 | 1 | 0.000105 |
| 4.484000e+01 | 1 | 0.000105 |
| 6.588000e+01 | 1 | 0.000105 |
| 1.983600e+02 | 1 | 0.000105 |
| 5.900000e+02 | 1 | 0.000105 |
| 1.160000e+03 | 1 | 0.000105 |
| 5.010000e+02 | 1 | 0.000105 |
| 1.399200e+02 | 1 | 0.000105 |
| 4.280000e+02 | 1 | 0.000105 |
| 7.196000e+01 | 1 | 0.000105 |
| 5.988000e+01 | 1 | 0.000105 |
| 2.410000e+02 | 1 | 0.000105 |
| 1.908000e+03 | 1 | 0.000105 |
| 2.064000e+03 | 1 | 0.000105 |
| 3.360000e+03 | 1 | 0.000105 |
| 3.602400e+02 | 1 | 0.000105 |
| 2.520000e+01 | 1 | 0.000105 |
| 2.803600e+02 | 1 | 0.000105 |
| 1.442430e+03 | 1 | 0.000105 |
| 6.006000e+01 | 1 | 0.000105 |
| 2.193600e+02 | 1 | 0.000105 |
| 3.006000e+01 | 1 | 0.000105 |
| 2.705000e+01 | 1 | 0.000105 |
| 2.308000e+02 | 1 | 0.000105 |
| 1.730000e+01 | 1 | 0.000105 |
| 3.860000e+02 | 1 | 0.000105 |
| 8.428800e+02 | 1 | 0.000105 |
| 6.132000e+01 | 1 | 0.000105 |
| 2.132000e+01 | 1 | 0.000105 |
| 2.451600e+02 | 1 | 0.000105 |
| 5.493000e+01 | 1 | 0.000105 |
| 6.600000e+03 | 1 | 0.000105 |
| 2.352000e+03 | 1 | 0.000105 |
| 4.666400e+02 | 1 | 0.000105 |
| 2.210000e+02 | 1 | 0.000105 |
| 1.752500e+02 | 1 | 0.000105 |
| 4.332000e+01 | 1 | 0.000105 |
| 9.014400e+02 | 1 | 0.000105 |
| 4.006000e+01 | 1 | 0.000105 |
| 4.059600e+02 | 1 | 0.000105 |
| 2.196000e+02 | 1 | 0.000105 |
| 1.009600e+02 | 1 | 0.000105 |
| 2.524200e+03 | 1 | 0.000105 |
| 4.447200e+02 | 1 | 0.000105 |
| 2.401000e+02 | 1 | 0.000105 |
| 3.000100e+02 | 1 | 0.000105 |
| 6.480000e+01 | 1 | 0.000105 |
| 1.153200e+02 | 1 | 0.000105 |
| 5.556000e+01 | 1 | 0.000105 |
| 2.115600e+02 | 1 | 0.000105 |
| 7.500000e+00 | 1 | 0.000105 |
| 1.121200e+02 | 1 | 0.000105 |
| 2.530000e+02 | 1 | 0.000105 |
| 2.430000e+02 | 1 | 0.000105 |
| 1.200800e+02 | 1 | 0.000105 |
| 2.598000e+01 | 1 | 0.000105 |
| 3.820000e+02 | 1 | 0.000105 |
| 2.890000e+02 | 1 | 0.000105 |
| 1.500000e+04 | 1 | 0.000105 |
| 1.810000e+02 | 1 | 0.000105 |
| 1.620500e+02 | 1 | 0.000105 |
| 1.514400e+02 | 1 | 0.000105 |
| 1.586640e+03 | 1 | 0.000105 |
| 1.930000e+02 | 1 | 0.000105 |
| 4.620000e+03 | 1 | 0.000105 |
| 1.204000e+03 | 1 | 0.000105 |
| 4.760000e+02 | 1 | 0.000105 |
| 1.939200e+02 | 1 | 0.000105 |
| 2.420000e+02 | 1 | 0.000105 |
| 8.400000e+03 | 1 | 0.000105 |
# Vamos a realizar analisis por cada variable
var = "msf_valuetotalcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuetotalcont__c es 520. Lo que supone un 0.052310515218336046% El nº de vacios para la variable msf_valuetotalcont__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 120.0 | 112276 | 11.300556 |
| 60.0 | 61017 | 6.141349 |
| 180.0 | 59534 | 5.992085 |
| 0.0 | 55793 | 5.615554 |
| 240.0 | 41562 | 4.183207 |
| ... | ... | ... |
| 3420.0 | 1 | 0.000101 |
| 849.0 | 1 | 0.000101 |
| 657.0 | 1 | 0.000101 |
| 2318.0 | 1 | 0.000101 |
| 8400.0 | 1 | 0.000101 |
1697 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_valuedonorcont__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_valuedonorcont__c es 727868. Lo que supone un 73.22144248257658% El nº de vacios para la variable msf_valuedonorcont__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 30.00 | 28197 | 10.592571 |
| 60.00 | 25845 | 9.709011 |
| 100.00 | 24352 | 9.148146 |
| 50.00 | 24260 | 9.113585 |
| 20.00 | 24043 | 9.032067 |
| ... | ... | ... |
| 5.92 | 1 | 0.000376 |
| 55.06 | 1 | 0.000376 |
| 1160.00 | 1 | 0.000376 |
| 2550.00 | 1 | 0.000376 |
| 1.17 | 1 | 0.000376 |
2389 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lastyeardonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lastyeardonorvalue__c es 939961. Lo que supone un 94.5573926829661% El nº de vacios para la variable msf_lastyeardonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 100.00 | 5575 | 10.304419 |
| 50.00 | 5537 | 10.234183 |
| 20.00 | 5037 | 9.310020 |
| 30.00 | 4499 | 8.315620 |
| 60.00 | 4144 | 7.659464 |
| 1.00 | 2591 | 4.789014 |
| 10.00 | 2575 | 4.759440 |
| 200.00 | 2036 | 3.763192 |
| 40.00 | 2007 | 3.709591 |
| 150.00 | 1375 | 2.541449 |
| 300.00 | 1097 | 2.027614 |
| 120.00 | 1035 | 1.913018 |
| 90.00 | 1012 | 1.870506 |
| 25.00 | 982 | 1.815056 |
| 15.00 | 907 | 1.676432 |
| 80.00 | 833 | 1.539656 |
| 5.00 | 819 | 1.513779 |
| 250.00 | 636 | 1.175536 |
| 500.00 | 617 | 1.140417 |
| 70.00 | 443 | 0.818809 |
| 2.00 | 431 | 0.796629 |
| 400.00 | 417 | 0.770752 |
| 125.00 | 412 | 0.761510 |
| 1000.00 | 301 | 0.556346 |
| 110.00 | 262 | 0.484262 |
| 600.00 | 259 | 0.478717 |
| 180.00 | 255 | 0.471323 |
| 45.00 | 250 | 0.462082 |
| 160.00 | 250 | 0.462082 |
| 140.00 | 194 | 0.358575 |
| 39.00 | 190 | 0.351182 |
| 75.00 | 185 | 0.341940 |
| 130.00 | 176 | 0.325305 |
| 3.00 | 171 | 0.316064 |
| 350.00 | 169 | 0.312367 |
| 66.00 | 164 | 0.303126 |
| 240.00 | 154 | 0.284642 |
| 450.00 | 131 | 0.242131 |
| 55.00 | 122 | 0.225496 |
| 35.00 | 115 | 0.212558 |
| 16.00 | 113 | 0.208861 |
| 220.00 | 105 | 0.194074 |
| 2000.00 | 100 | 0.184833 |
| 12.00 | 100 | 0.184833 |
| 170.00 | 96 | 0.177439 |
| 26.00 | 90 | 0.166349 |
| 78.00 | 90 | 0.166349 |
| 800.00 | 89 | 0.164501 |
| 190.00 | 88 | 0.162653 |
| 51.00 | 83 | 0.153411 |
| 61.00 | 82 | 0.151563 |
| 175.00 | 82 | 0.151563 |
| 4.00 | 82 | 0.151563 |
| 210.00 | 80 | 0.147866 |
| 6.00 | 78 | 0.144169 |
| 8.00 | 77 | 0.142321 |
| 270.00 | 77 | 0.142321 |
| 700.00 | 77 | 0.142321 |
| 550.00 | 68 | 0.125686 |
| 21.00 | 68 | 0.125686 |
| 1500.00 | 66 | 0.121990 |
| 65.00 | 64 | 0.118293 |
| 31.00 | 62 | 0.114596 |
| 260.00 | 60 | 0.110900 |
| 900.00 | 59 | 0.109051 |
| 750.00 | 58 | 0.107203 |
| 24.00 | 58 | 0.107203 |
| 225.00 | 57 | 0.105355 |
| 101.00 | 55 | 0.101658 |
| 3000.00 | 55 | 0.101658 |
| 156.00 | 52 | 0.096113 |
| 325.00 | 48 | 0.088720 |
| 360.00 | 47 | 0.086871 |
| 650.00 | 47 | 0.086871 |
| 7.00 | 45 | 0.083175 |
| 1200.00 | 44 | 0.081326 |
| 99.00 | 44 | 0.081326 |
| 230.00 | 41 | 0.075781 |
| 85.00 | 39 | 0.072085 |
| 280.00 | 39 | 0.072085 |
| 32.00 | 37 | 0.068388 |
| 320.00 | 36 | 0.066540 |
| 11.00 | 32 | 0.059146 |
| 69.00 | 30 | 0.055450 |
| 275.00 | 30 | 0.055450 |
| 375.00 | 28 | 0.051753 |
| 105.00 | 28 | 0.051753 |
| 1100.00 | 27 | 0.049905 |
| 36.00 | 27 | 0.049905 |
| 340.00 | 27 | 0.049905 |
| 41.00 | 27 | 0.049905 |
| 34.00 | 26 | 0.048056 |
| 18.00 | 26 | 0.048056 |
| 290.00 | 26 | 0.048056 |
| 185.00 | 26 | 0.048056 |
| 46.00 | 25 | 0.046208 |
| 1300.00 | 24 | 0.044360 |
| 850.00 | 24 | 0.044360 |
| 14.00 | 24 | 0.044360 |
| 205.00 | 23 | 0.042512 |
| 370.00 | 23 | 0.042512 |
| 91.00 | 23 | 0.042512 |
| 138.00 | 22 | 0.040663 |
| 1400.00 | 22 | 0.040663 |
| 245.00 | 22 | 0.040663 |
| 165.00 | 21 | 0.038815 |
| 420.00 | 21 | 0.038815 |
| 102.00 | 21 | 0.038815 |
| 52.00 | 20 | 0.036967 |
| 151.00 | 19 | 0.035118 |
| 89.00 | 19 | 0.035118 |
| 4000.00 | 19 | 0.035118 |
| 2500.00 | 19 | 0.035118 |
| 126.00 | 19 | 0.035118 |
| 330.00 | 19 | 0.035118 |
| 425.00 | 18 | 0.033270 |
| 62.00 | 18 | 0.033270 |
| 56.00 | 18 | 0.033270 |
| 310.00 | 18 | 0.033270 |
| 121.00 | 18 | 0.033270 |
| 115.00 | 18 | 0.033270 |
| 111.00 | 18 | 0.033270 |
| 5000.00 | 17 | 0.031422 |
| 9.00 | 17 | 0.031422 |
| 95.00 | 17 | 0.031422 |
| 390.00 | 17 | 0.031422 |
| 675.00 | 16 | 0.029573 |
| 81.00 | 16 | 0.029573 |
| 525.00 | 16 | 0.029573 |
| 117.00 | 16 | 0.029573 |
| 128.00 | 15 | 0.027725 |
| 76.00 | 15 | 0.027725 |
| 950.00 | 15 | 0.027725 |
| 48.00 | 15 | 0.027725 |
| 1600.00 | 15 | 0.027725 |
| 6000.00 | 14 | 0.025877 |
| 42.00 | 14 | 0.025877 |
| 1250.00 | 14 | 0.025877 |
| 178.00 | 14 | 0.025877 |
| 22.00 | 14 | 0.025877 |
| 86.00 | 13 | 0.024028 |
| 145.00 | 13 | 0.024028 |
| 10000.00 | 13 | 0.024028 |
| 72.00 | 13 | 0.024028 |
| 135.00 | 12 | 0.022180 |
| 1050.00 | 12 | 0.022180 |
| 295.00 | 12 | 0.022180 |
| 380.00 | 12 | 0.022180 |
| 480.00 | 12 | 0.022180 |
| 520.00 | 12 | 0.022180 |
| 96.00 | 12 | 0.022180 |
| 195.00 | 12 | 0.022180 |
| 27.00 | 11 | 0.020332 |
| 410.00 | 11 | 0.020332 |
| 475.00 | 11 | 0.020332 |
| 92.00 | 11 | 0.020332 |
| 273.00 | 11 | 0.020332 |
| 1450.00 | 11 | 0.020332 |
| 64.00 | 11 | 0.020332 |
| 139.00 | 10 | 0.018483 |
| 560.00 | 10 | 0.018483 |
| 158.00 | 10 | 0.018483 |
| 116.00 | 10 | 0.018483 |
| 625.00 | 10 | 0.018483 |
| 430.00 | 10 | 0.018483 |
| 28.00 | 10 | 0.018483 |
| 63.00 | 10 | 0.018483 |
| 1800.00 | 10 | 0.018483 |
| 306.00 | 10 | 0.018483 |
| 155.00 | 10 | 0.018483 |
| 440.00 | 9 | 0.016635 |
| 490.00 | 9 | 0.016635 |
| 148.00 | 9 | 0.016635 |
| 201.00 | 9 | 0.016635 |
| 53.00 | 9 | 0.016635 |
| 215.00 | 9 | 0.016635 |
| 198.00 | 9 | 0.016635 |
| 315.00 | 9 | 0.016635 |
| 33.00 | 9 | 0.016635 |
| 456.00 | 9 | 0.016635 |
| 59.00 | 9 | 0.016635 |
| 47.00 | 8 | 0.014787 |
| 365.00 | 8 | 0.014787 |
| 2400.00 | 8 | 0.014787 |
| 305.00 | 8 | 0.014787 |
| 129.00 | 8 | 0.014787 |
| 285.00 | 8 | 0.014787 |
| 119.00 | 8 | 0.014787 |
| 281.00 | 8 | 0.014787 |
| 217.00 | 8 | 0.014787 |
| 301.00 | 8 | 0.014787 |
| 166.00 | 8 | 0.014787 |
| 79.00 | 8 | 0.014787 |
| 118.00 | 8 | 0.014787 |
| 256.00 | 8 | 0.014787 |
| 211.00 | 8 | 0.014787 |
| 23.00 | 8 | 0.014787 |
| 142.00 | 8 | 0.014787 |
| 131.00 | 8 | 0.014787 |
| 415.00 | 8 | 0.014787 |
| 68.00 | 8 | 0.014787 |
| 17.00 | 7 | 0.012938 |
| 501.00 | 7 | 0.012938 |
| 356.00 | 7 | 0.012938 |
| 202.00 | 7 | 0.012938 |
| 77.00 | 7 | 0.012938 |
| 13.00 | 7 | 0.012938 |
| 575.00 | 7 | 0.012938 |
| 67.00 | 7 | 0.012938 |
| 84.00 | 7 | 0.012938 |
| 203.00 | 7 | 0.012938 |
| 239.00 | 7 | 0.012938 |
| 255.00 | 7 | 0.012938 |
| 132.00 | 7 | 0.012938 |
| 71.00 | 7 | 0.012938 |
| 73.00 | 7 | 0.012938 |
| 1700.00 | 7 | 0.012938 |
| 725.00 | 7 | 0.012938 |
| 470.00 | 7 | 0.012938 |
| 216.00 | 6 | 0.011090 |
| 186.00 | 6 | 0.011090 |
| 104.00 | 6 | 0.011090 |
| 199.00 | 6 | 0.011090 |
| 38.00 | 6 | 0.011090 |
| 124.00 | 6 | 0.011090 |
| 775.00 | 6 | 0.011090 |
| 58.00 | 6 | 0.011090 |
| 2100.00 | 6 | 0.011090 |
| 161.00 | 6 | 0.011090 |
| 106.00 | 5 | 0.009242 |
| 610.00 | 5 | 0.009242 |
| 19.00 | 5 | 0.009242 |
| 169.00 | 5 | 0.009242 |
| 2700.00 | 5 | 0.009242 |
| 82.00 | 5 | 0.009242 |
| 620.00 | 5 | 0.009242 |
| 510.00 | 5 | 0.009242 |
| 2800.00 | 5 | 0.009242 |
| 460.00 | 5 | 0.009242 |
| 660.00 | 5 | 0.009242 |
| 168.00 | 5 | 0.009242 |
| 188.00 | 5 | 0.009242 |
| 74.00 | 5 | 0.009242 |
| 1350.00 | 5 | 0.009242 |
| 606.00 | 5 | 0.009242 |
| 326.00 | 5 | 0.009242 |
| 141.00 | 5 | 0.009242 |
| 94.00 | 5 | 0.009242 |
| 136.00 | 5 | 0.009242 |
| 395.00 | 5 | 0.009242 |
| 54.00 | 5 | 0.009242 |
| 108.00 | 5 | 0.009242 |
| 3500.00 | 5 | 0.009242 |
| 98.00 | 5 | 0.009242 |
| 374.00 | 5 | 0.009242 |
| 825.00 | 5 | 0.009242 |
| 20000.00 | 5 | 0.009242 |
| 181.00 | 5 | 0.009242 |
| 925.00 | 5 | 0.009242 |
| 264.00 | 5 | 0.009242 |
| 406.00 | 5 | 0.009242 |
| 171.00 | 5 | 0.009242 |
| 149.00 | 4 | 0.007393 |
| 258.00 | 4 | 0.007393 |
| 465.00 | 4 | 0.007393 |
| 1150.00 | 4 | 0.007393 |
| 353.00 | 4 | 0.007393 |
| 540.00 | 4 | 0.007393 |
| 381.00 | 4 | 0.007393 |
| 299.00 | 4 | 0.007393 |
| 123.00 | 4 | 0.007393 |
| 265.00 | 4 | 0.007393 |
| 780.00 | 4 | 0.007393 |
| 1140.00 | 4 | 0.007393 |
| 351.00 | 4 | 0.007393 |
| 2300.00 | 4 | 0.007393 |
| 580.00 | 4 | 0.007393 |
| 206.00 | 4 | 0.007393 |
| 345.00 | 4 | 0.007393 |
| 146.00 | 4 | 0.007393 |
| 690.00 | 4 | 0.007393 |
| 530.00 | 4 | 0.007393 |
| 630.00 | 4 | 0.007393 |
| 316.00 | 4 | 0.007393 |
| 176.00 | 4 | 0.007393 |
| 7000.00 | 4 | 0.007393 |
| 401.00 | 4 | 0.007393 |
| 246.00 | 4 | 0.007393 |
| 820.00 | 4 | 0.007393 |
| 7500.00 | 4 | 0.007393 |
| 177.00 | 4 | 0.007393 |
| 87.00 | 4 | 0.007393 |
| 278.00 | 4 | 0.007393 |
| 37.00 | 4 | 0.007393 |
| 189.00 | 4 | 0.007393 |
| 3200.00 | 4 | 0.007393 |
| 251.00 | 4 | 0.007393 |
| 1750.00 | 4 | 0.007393 |
| 109.00 | 4 | 0.007393 |
| 147.00 | 4 | 0.007393 |
| 302.00 | 4 | 0.007393 |
| 221.00 | 3 | 0.005545 |
| 235.00 | 3 | 0.005545 |
| 261.00 | 3 | 0.005545 |
| 505.00 | 3 | 0.005545 |
| 152.00 | 3 | 0.005545 |
| 134.00 | 3 | 0.005545 |
| 376.00 | 3 | 0.005545 |
| 262.00 | 3 | 0.005545 |
| 373.00 | 3 | 0.005545 |
| 122.00 | 3 | 0.005545 |
| 570.00 | 3 | 0.005545 |
| 9000.00 | 3 | 0.005545 |
| 399.00 | 3 | 0.005545 |
| 539.00 | 3 | 0.005545 |
| 271.00 | 3 | 0.005545 |
| 573.00 | 3 | 0.005545 |
| 468.00 | 3 | 0.005545 |
| 226.00 | 3 | 0.005545 |
| 197.00 | 3 | 0.005545 |
| 249.00 | 3 | 0.005545 |
| 2200.00 | 3 | 0.005545 |
| 43.00 | 3 | 0.005545 |
| 1650.00 | 3 | 0.005545 |
| 44.00 | 3 | 0.005545 |
| 710.00 | 3 | 0.005545 |
| 204.00 | 3 | 0.005545 |
| 252.00 | 3 | 0.005545 |
| 112.00 | 3 | 0.005545 |
| 11000.00 | 3 | 0.005545 |
| 524.00 | 3 | 0.005545 |
| 1550.00 | 3 | 0.005545 |
| 489.00 | 3 | 0.005545 |
| 231.00 | 3 | 0.005545 |
| 556.00 | 3 | 0.005545 |
| 361.00 | 3 | 0.005545 |
| 162.00 | 3 | 0.005545 |
| 445.00 | 3 | 0.005545 |
| 107.00 | 3 | 0.005545 |
| 473.00 | 3 | 0.005545 |
| 143.00 | 3 | 0.005545 |
| 790.00 | 3 | 0.005545 |
| 144.00 | 3 | 0.005545 |
| 615.00 | 3 | 0.005545 |
| 334.00 | 3 | 0.005545 |
| 297.00 | 3 | 0.005545 |
| 506.00 | 3 | 0.005545 |
| 8000.00 | 3 | 0.005545 |
| 114.00 | 3 | 0.005545 |
| 975.00 | 3 | 0.005545 |
| 307.00 | 3 | 0.005545 |
| 3400.00 | 3 | 0.005545 |
| 1325.00 | 3 | 0.005545 |
| 2600.00 | 3 | 0.005545 |
| 223.00 | 3 | 0.005545 |
| 229.00 | 2 | 0.003697 |
| 338.00 | 2 | 0.003697 |
| 254.00 | 2 | 0.003697 |
| 93.00 | 2 | 0.003697 |
| 720.00 | 2 | 0.003697 |
| 354.00 | 2 | 0.003697 |
| 496.00 | 2 | 0.003697 |
| 920.00 | 2 | 0.003697 |
| 405.00 | 2 | 0.003697 |
| 1003.00 | 2 | 0.003697 |
| 687.00 | 2 | 0.003697 |
| 222.00 | 2 | 0.003697 |
| 1025.00 | 2 | 0.003697 |
| 756.00 | 2 | 0.003697 |
| 344.00 | 2 | 0.003697 |
| 194.00 | 2 | 0.003697 |
| 1580.00 | 2 | 0.003697 |
| 266.00 | 2 | 0.003697 |
| 179.00 | 2 | 0.003697 |
| 49.00 | 2 | 0.003697 |
| 5250.00 | 2 | 0.003697 |
| 103.00 | 2 | 0.003697 |
| 2900.00 | 2 | 0.003697 |
| 133.00 | 2 | 0.003697 |
| 187.00 | 2 | 0.003697 |
| 3100.00 | 2 | 0.003697 |
| 269.00 | 2 | 0.003697 |
| 238.00 | 2 | 0.003697 |
| 875.00 | 2 | 0.003697 |
| 219.00 | 2 | 0.003697 |
| 3650.00 | 2 | 0.003697 |
| 1175.00 | 2 | 0.003697 |
| 1075.00 | 2 | 0.003697 |
| 83.00 | 2 | 0.003697 |
| 324.00 | 2 | 0.003697 |
| 276.00 | 2 | 0.003697 |
| 153.00 | 2 | 0.003697 |
| 173.00 | 2 | 0.003697 |
| 429.00 | 2 | 0.003697 |
| 99.99 | 2 | 0.003697 |
| 5400.00 | 2 | 0.003697 |
| 25000.00 | 2 | 0.003697 |
| 564.00 | 2 | 0.003697 |
| 303.00 | 2 | 0.003697 |
| 57.00 | 2 | 0.003697 |
| 12000.00 | 2 | 0.003697 |
| 348.00 | 2 | 0.003697 |
| 1460.00 | 2 | 0.003697 |
| 159.00 | 2 | 0.003697 |
| 241.00 | 2 | 0.003697 |
| 207.00 | 2 | 0.003697 |
| 355.00 | 2 | 0.003697 |
| 565.00 | 2 | 0.003697 |
| 196.00 | 2 | 0.003697 |
| 6500.00 | 2 | 0.003697 |
| 331.00 | 2 | 0.003697 |
| 640.00 | 2 | 0.003697 |
| 495.00 | 2 | 0.003697 |
| 384.00 | 2 | 0.003697 |
| 1125.00 | 2 | 0.003697 |
| 455.00 | 2 | 0.003697 |
| 595.00 | 2 | 0.003697 |
| 16000.00 | 2 | 0.003697 |
| 483.00 | 2 | 0.003697 |
| 840.00 | 2 | 0.003697 |
| 816.00 | 2 | 0.003697 |
| 1020.00 | 2 | 0.003697 |
| 1950.00 | 2 | 0.003697 |
| 670.00 | 2 | 0.003697 |
| 444.00 | 2 | 0.003697 |
| 1968.00 | 2 | 0.003697 |
| 234.00 | 2 | 0.003697 |
| 1260.00 | 2 | 0.003697 |
| 689.00 | 2 | 0.003697 |
| 263.00 | 2 | 0.003697 |
| 172.00 | 2 | 0.003697 |
| 448.00 | 2 | 0.003697 |
| 514.00 | 2 | 0.003697 |
| 4500.00 | 2 | 0.003697 |
| 97.00 | 2 | 0.003697 |
| 590.00 | 2 | 0.003697 |
| 323.00 | 2 | 0.003697 |
| 2750.00 | 2 | 0.003697 |
| 228.00 | 2 | 0.003697 |
| 30.05 | 2 | 0.003697 |
| 244.00 | 2 | 0.003697 |
| 770.00 | 2 | 0.003697 |
| 402.00 | 2 | 0.003697 |
| 3600.00 | 2 | 0.003697 |
| 208.00 | 2 | 0.003697 |
| 337.00 | 2 | 0.003697 |
| 333.00 | 2 | 0.003697 |
| 218.00 | 2 | 0.003697 |
| 378.00 | 2 | 0.003697 |
| 631.00 | 2 | 0.003697 |
| 1900.00 | 2 | 0.003697 |
| 645.00 | 1 | 0.001848 |
| 3300.00 | 1 | 0.001848 |
| 651.00 | 1 | 0.001848 |
| 404.00 | 1 | 0.001848 |
| 1010.00 | 1 | 0.001848 |
| 328.00 | 1 | 0.001848 |
| 127.00 | 1 | 0.001848 |
| 873.00 | 1 | 0.001848 |
| 643.00 | 1 | 0.001848 |
| 5636.00 | 1 | 0.001848 |
| 382.00 | 1 | 0.001848 |
| 157.00 | 1 | 0.001848 |
| 343.00 | 1 | 0.001848 |
| 686.00 | 1 | 0.001848 |
| 705.00 | 1 | 0.001848 |
| 540.90 | 1 | 0.001848 |
| 379.00 | 1 | 0.001848 |
| 5003.00 | 1 | 0.001848 |
| 209.00 | 1 | 0.001848 |
| 385.00 | 1 | 0.001848 |
| 1559.18 | 1 | 0.001848 |
| 1270.00 | 1 | 0.001848 |
| 416.00 | 1 | 0.001848 |
| 9500.00 | 1 | 0.001848 |
| 28277.52 | 1 | 0.001848 |
| 227.00 | 1 | 0.001848 |
| 945.70 | 1 | 0.001848 |
| 566.00 | 1 | 0.001848 |
| 2190.00 | 1 | 0.001848 |
| 417.00 | 1 | 0.001848 |
| 2020.00 | 1 | 0.001848 |
| 1015.00 | 1 | 0.001848 |
| 494.00 | 1 | 0.001848 |
| 4100.00 | 1 | 0.001848 |
| 248.00 | 1 | 0.001848 |
| 936.00 | 1 | 0.001848 |
| 457.00 | 1 | 0.001848 |
| 293.00 | 1 | 0.001848 |
| 3420.00 | 1 | 0.001848 |
| 752.00 | 1 | 0.001848 |
| 584.00 | 1 | 0.001848 |
| 783.00 | 1 | 0.001848 |
| 1286.80 | 1 | 0.001848 |
| 74000.00 | 1 | 0.001848 |
| 308.00 | 1 | 0.001848 |
| 1056.00 | 1 | 0.001848 |
| 312.00 | 1 | 0.001848 |
| 639.00 | 1 | 0.001848 |
| 133.03 | 1 | 0.001848 |
| 461.00 | 1 | 0.001848 |
| 492.00 | 1 | 0.001848 |
| 6550.00 | 1 | 0.001848 |
| 13500.00 | 1 | 0.001848 |
| 692.85 | 1 | 0.001848 |
| 272.00 | 1 | 0.001848 |
| 1101.00 | 1 | 0.001848 |
| 228.70 | 1 | 0.001848 |
| 841.00 | 1 | 0.001848 |
| 795.00 | 1 | 0.001848 |
| 523.00 | 1 | 0.001848 |
| 346.00 | 1 | 0.001848 |
| 398.00 | 1 | 0.001848 |
| 7.20 | 1 | 0.001848 |
| 814.00 | 1 | 0.001848 |
| 1111.00 | 1 | 0.001848 |
| 247.00 | 1 | 0.001848 |
| 880.00 | 1 | 0.001848 |
| 2001.00 | 1 | 0.001848 |
| 11.11 | 1 | 0.001848 |
| 4301.00 | 1 | 0.001848 |
| 469.00 | 1 | 0.001848 |
| 162.67 | 1 | 0.001848 |
| 504.00 | 1 | 0.001848 |
| 393.00 | 1 | 0.001848 |
| 895.00 | 1 | 0.001848 |
| 439.00 | 1 | 0.001848 |
| 2575.55 | 1 | 0.001848 |
| 267.00 | 1 | 0.001848 |
| 164.00 | 1 | 0.001848 |
| 1773.00 | 1 | 0.001848 |
| 397.00 | 1 | 0.001848 |
| 507.00 | 1 | 0.001848 |
| 458.00 | 1 | 0.001848 |
| 730.00 | 1 | 0.001848 |
| 1554.00 | 1 | 0.001848 |
| 224.00 | 1 | 0.001848 |
| 739.00 | 1 | 0.001848 |
| 502.00 | 1 | 0.001848 |
| 709.00 | 1 | 0.001848 |
| 0.03 | 1 | 0.001848 |
| 467.00 | 1 | 0.001848 |
| 1443.00 | 1 | 0.001848 |
| 309.00 | 1 | 0.001848 |
| 1128.00 | 1 | 0.001848 |
| 1301.00 | 1 | 0.001848 |
| 243.00 | 1 | 0.001848 |
| 2350.00 | 1 | 0.001848 |
| 662.00 | 1 | 0.001848 |
| 890.00 | 1 | 0.001848 |
| 718.00 | 1 | 0.001848 |
| 1850.00 | 1 | 0.001848 |
| 1675.00 | 1 | 0.001848 |
| 588.00 | 1 | 0.001848 |
| 985.00 | 1 | 0.001848 |
| 233.00 | 1 | 0.001848 |
| 1830.00 | 1 | 0.001848 |
| 1336.00 | 1 | 0.001848 |
| 16.60 | 1 | 0.001848 |
| 182.00 | 1 | 0.001848 |
| 1.50 | 1 | 0.001848 |
| 7600.00 | 1 | 0.001848 |
| 418.32 | 1 | 0.001848 |
| 657.00 | 1 | 0.001848 |
| 4.61 | 1 | 0.001848 |
| 3262.00 | 1 | 0.001848 |
| 287.00 | 1 | 0.001848 |
| 1215.00 | 1 | 0.001848 |
| 1080.00 | 1 | 0.001848 |
| 3936.00 | 1 | 0.001848 |
| 648.00 | 1 | 0.001848 |
| 377.00 | 1 | 0.001848 |
| 180.50 | 1 | 0.001848 |
| 274.00 | 1 | 0.001848 |
| 184.00 | 1 | 0.001848 |
| 929.00 | 1 | 0.001848 |
| 193.00 | 1 | 0.001848 |
| 383.00 | 1 | 0.001848 |
| 1570.00 | 1 | 0.001848 |
| 1340.00 | 1 | 0.001848 |
| 3450.00 | 1 | 0.001848 |
| 18000.00 | 1 | 0.001848 |
| 369.00 | 1 | 0.001848 |
| 893.00 | 1 | 0.001848 |
| 591.00 | 1 | 0.001848 |
| 213.00 | 1 | 0.001848 |
| 7.38 | 1 | 0.001848 |
| 327.00 | 1 | 0.001848 |
| 3.13 | 1 | 0.001848 |
| 579.00 | 1 | 0.001848 |
| 434.00 | 1 | 0.001848 |
| 1418.00 | 1 | 0.001848 |
| 699.00 | 1 | 0.001848 |
| 17000.00 | 1 | 0.001848 |
| 431.00 | 1 | 0.001848 |
| 680.00 | 1 | 0.001848 |
| 0.57 | 1 | 0.001848 |
| 1120.00 | 1 | 0.001848 |
| 726.00 | 1 | 0.001848 |
| 486.00 | 1 | 0.001848 |
| 1620.00 | 1 | 0.001848 |
| 51.96 | 1 | 0.001848 |
| 516.00 | 1 | 0.001848 |
| 2250.00 | 1 | 0.001848 |
| 576.20 | 1 | 0.001848 |
| 488.00 | 1 | 0.001848 |
| 341.00 | 1 | 0.001848 |
| 368.00 | 1 | 0.001848 |
| 5200.00 | 1 | 0.001848 |
| 359.00 | 1 | 0.001848 |
| 331.10 | 1 | 0.001848 |
| 268.00 | 1 | 0.001848 |
| 15000.00 | 1 | 0.001848 |
| 352.00 | 1 | 0.001848 |
| 41.50 | 1 | 0.001848 |
| 113.00 | 1 | 0.001848 |
| 623.00 | 1 | 0.001848 |
| 1526.00 | 1 | 0.001848 |
| 568.00 | 1 | 0.001848 |
| 44.44 | 1 | 0.001848 |
| 40000.00 | 1 | 0.001848 |
| 810.00 | 1 | 0.001848 |
| 968.00 | 1 | 0.001848 |
| 29.00 | 1 | 0.001848 |
| 1171.00 | 1 | 0.001848 |
| 6845.00 | 1 | 0.001848 |
| 2802.00 | 1 | 0.001848 |
| 1515.60 | 1 | 0.001848 |
| 1473.00 | 1 | 0.001848 |
| 1273.00 | 1 | 0.001848 |
| 655.00 | 1 | 0.001848 |
| 4180.00 | 1 | 0.001848 |
| 298.00 | 1 | 0.001848 |
| 324.98 | 1 | 0.001848 |
| 4300.00 | 1 | 0.001848 |
| 2185.00 | 1 | 0.001848 |
| 881.00 | 1 | 0.001848 |
| 1217.00 | 1 | 0.001848 |
| 1001.00 | 1 | 0.001848 |
| 304.00 | 1 | 0.001848 |
| 695.00 | 1 | 0.001848 |
| 581.00 | 1 | 0.001848 |
| 421.00 | 1 | 0.001848 |
| 1625.00 | 1 | 0.001848 |
| 1.59 | 1 | 0.001848 |
| 0.02 | 1 | 0.001848 |
| 414.00 | 1 | 0.001848 |
| 889.00 | 1 | 0.001848 |
| 236.00 | 1 | 0.001848 |
| 259.00 | 1 | 0.001848 |
| 2450.00 | 1 | 0.001848 |
| 150.25 | 1 | 0.001848 |
| 558.00 | 1 | 0.001848 |
| 3700.00 | 1 | 0.001848 |
| 860.00 | 1 | 0.001848 |
| 1875.00 | 1 | 0.001848 |
| 7300.00 | 1 | 0.001848 |
| 2050.00 | 1 | 0.001848 |
| 508.00 | 1 | 0.001848 |
| 3901.00 | 1 | 0.001848 |
| 1375.00 | 1 | 0.001848 |
| 1315.00 | 1 | 0.001848 |
| 1525.00 | 1 | 0.001848 |
| 548.00 | 1 | 0.001848 |
| 371.00 | 1 | 0.001848 |
| 191.00 | 1 | 0.001848 |
| 5600.00 | 1 | 0.001848 |
| 451.00 | 1 | 0.001848 |
| 449.00 | 1 | 0.001848 |
| 167.00 | 1 | 0.001848 |
| 3050.00 | 1 | 0.001848 |
| 100000.00 | 1 | 0.001848 |
| 2570.00 | 1 | 0.001848 |
| 10.10 | 1 | 0.001848 |
| 48.08 | 1 | 0.001848 |
| 339.00 | 1 | 0.001848 |
| 5700.00 | 1 | 0.001848 |
| 2575.00 | 1 | 0.001848 |
| 1578.00 | 1 | 0.001848 |
| 317.00 | 1 | 0.001848 |
| 294.00 | 1 | 0.001848 |
| 745.00 | 1 | 0.001848 |
| 485.00 | 1 | 0.001848 |
| 459.00 | 1 | 0.001848 |
| 335.00 | 1 | 0.001848 |
| 363.00 | 1 | 0.001848 |
| 1209.00 | 1 | 0.001848 |
| 1070.00 | 1 | 0.001848 |
| 2177.00 | 1 | 0.001848 |
| 389.00 | 1 | 0.001848 |
| 1725.00 | 1 | 0.001848 |
| 1379.00 | 1 | 0.001848 |
| 970.00 | 1 | 0.001848 |
| 674.00 | 1 | 0.001848 |
| 665.00 | 1 | 0.001848 |
| 518.00 | 1 | 0.001848 |
| 137.00 | 1 | 0.001848 |
| 823.00 | 1 | 0.001848 |
| 479.00 | 1 | 0.001848 |
| 1153.00 | 1 | 0.001848 |
| 958.00 | 1 | 0.001848 |
| 3.30 | 1 | 0.001848 |
| 806.00 | 1 | 0.001848 |
| 296.00 | 1 | 0.001848 |
# Vamos a realizar analisis por cada variable
var = "msf_maximumdonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_maximumdonorvalue__c es 727510. Lo que supone un 73.18542870479165% El nº de vacios para la variable msf_maximumdonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 60.00 | 32588 | 12.225665 |
| 100.00 | 30759 | 11.539500 |
| 30.00 | 28663 | 10.753168 |
| 50.00 | 22916 | 8.597132 |
| 20.00 | 19944 | 7.482161 |
| ... | ... | ... |
| 26.14 | 1 | 0.000375 |
| 17.63 | 1 | 0.000375 |
| 19.48 | 1 | 0.000375 |
| 2800.00 | 1 | 0.000375 |
| 1.17 | 1 | 0.000375 |
2101 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_averagedonorvalue__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_averagedonorvalue__c es 727510. Lo que supone un 73.18542870479165% El nº de vacios para la variable msf_averagedonorvalue__c es 0. Lo que supone un 0.0%
['npo02__best_gift_year__c', 'msf_birthyear__c', 'msf_firstcampaigncolaborationchannel__c', 'npo02__averageamount__c', 'msf_isactivedonor__c', 'msf_isactiverecurringdonor__c', 'msf_datefirstdonation__c', 'msf_datelastdonation__c', 'npsp__largest_soft_credit_date__c', 'npsp__first_soft_credit_date__c', 'npsp__last_soft_credit_date__c', 'msf_lastrecurringdonationdate__c', 'npo02__lastclosedate__c', 'npsp__first_soft_credit_amount__c', 'npsp__last_soft_credit_amount__c', 'msf_annualizedquotachange__c', 'npsp__largest_soft_credit_amount__c', 'npo02__soft_credit_last_year__c', 'npo02__soft_credit_this_year__c', 'npo02__soft_credit_two_years_ago__c', 'msf_recencydonorcont__c', 'msf_valuedonorcont__c', 'msf_lastyeardonorvalue__c', 'msf_maximumdonorvalue__c', 'msf_averagedonorvalue__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 30.00 | 21243 | 7.969492 |
| 60.00 | 17261 | 6.475611 |
| 20.00 | 15879 | 5.957142 |
| 50.00 | 13184 | 4.946090 |
| 10.00 | 13002 | 4.877811 |
| ... | ... | ... |
| 1392.86 | 1 | 0.000375 |
| 53.73 | 1 | 0.000375 |
| 644.44 | 1 | 0.000375 |
| 467.74 | 1 | 0.000375 |
| 128.50 | 1 | 0.000375 |
17627 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_lifetime__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_lifetime__c es 57038. Lo que supone un 5.73785993658356% El nº de vacios para la variable msf_lifetime__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 154514 | 16.489831 |
| 1.0 | 75815 | 8.091024 |
| 2.0 | 59321 | 6.330774 |
| 6.0 | 56779 | 6.059490 |
| 3.0 | 55521 | 5.925236 |
| 7.0 | 55381 | 5.910295 |
| 5.0 | 52727 | 5.627058 |
| 8.0 | 52462 | 5.598777 |
| 4.0 | 52176 | 5.568255 |
| 9.0 | 36174 | 3.860512 |
| 10.0 | 29908 | 3.191800 |
| 11.0 | 28918 | 3.086147 |
| 12.0 | 26702 | 2.849654 |
| 13.0 | 24708 | 2.636853 |
| 14.0 | 22563 | 2.407937 |
| 17.0 | 17781 | 1.897599 |
| 18.0 | 17439 | 1.861101 |
| 16.0 | 16506 | 1.761531 |
| 15.0 | 15327 | 1.635707 |
| 19.0 | 13700 | 1.462073 |
| 20.0 | 10915 | 1.164856 |
| 28.0 | 10427 | 1.112776 |
| 23.0 | 8673 | 0.925588 |
| 22.0 | 6923 | 0.738827 |
| 24.0 | 6358 | 0.678530 |
| 21.0 | 5587 | 0.596248 |
| 29.0 | 5515 | 0.588564 |
| 25.0 | 4929 | 0.526026 |
| 26.0 | 4213 | 0.449614 |
| 27.0 | 4134 | 0.441183 |
| 30.0 | 3745 | 0.399669 |
| 31.0 | 707 | 0.075451 |
| 32.0 | 182 | 0.019423 |
| 34.0 | 138 | 0.014727 |
| 33.0 | 97 | 0.010352 |
| 35.0 | 47 | 0.005016 |
| 36.0 | 14 | 0.001494 |
# Vamos a realizar analisis por cada variable
var = "msf_commitment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_contactos)
El nº de nulos para la variable msf_commitment__c es 20360. Lo que supone un 2.0481578650871572% El nº de vacios para la variable msf_commitment__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0.0 | 645886 | 66.332890 |
| 1.0 | 185025 | 19.002181 |
| 2.0 | 72014 | 7.395882 |
| 3.0 | 32348 | 3.322160 |
| 4.0 | 16454 | 1.689836 |
| 5.0 | 8691 | 0.892571 |
| 6.0 | 5006 | 0.514119 |
| 7.0 | 2974 | 0.305432 |
| 8.0 | 1781 | 0.182910 |
| 9.0 | 1118 | 0.114819 |
| 10.0 | 679 | 0.069734 |
| 11.0 | 489 | 0.050221 |
| 12.0 | 305 | 0.031324 |
| 13.0 | 219 | 0.022491 |
| 14.0 | 155 | 0.015919 |
| 16.0 | 105 | 0.010784 |
| 15.0 | 104 | 0.010681 |
| 17.0 | 68 | 0.006984 |
| 18.0 | 46 | 0.004724 |
| 19.0 | 35 | 0.003595 |
| 20.0 | 27 | 0.002773 |
| 21.0 | 26 | 0.002670 |
| 22.0 | 20 | 0.002054 |
| 23.0 | 16 | 0.001643 |
| 24.0 | 14 | 0.001438 |
| 25.0 | 13 | 0.001335 |
| 29.0 | 12 | 0.001232 |
| 26.0 | 10 | 0.001027 |
| 30.0 | 8 | 0.000822 |
| 28.0 | 8 | 0.000822 |
| 27.0 | 7 | 0.000719 |
| 32.0 | 7 | 0.000719 |
| 31.0 | 6 | 0.000616 |
| 33.0 | 4 | 0.000411 |
| 34.0 | 3 | 0.000308 |
| 43.0 | 2 | 0.000205 |
| 38.0 | 2 | 0.000205 |
| 36.0 | 2 | 0.000205 |
| 42.0 | 2 | 0.000205 |
| 61.0 | 2 | 0.000205 |
| 47.0 | 1 | 0.000103 |
| 72.0 | 1 | 0.000103 |
| 57.0 | 1 | 0.000103 |
| 80.0 | 1 | 0.000103 |
| 56.0 | 1 | 0.000103 |
| 37.0 | 1 | 0.000103 |
| 71.0 | 1 | 0.000103 |
| 35.0 | 1 | 0.000103 |
| 46.0 | 1 | 0.000103 |
| 45.0 | 1 | 0.000103 |
| 54.0 | 1 | 0.000103 |
# Vamos a analizar la tabla Campañas
df = df_Campaign
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_campaign=list()
# Vamos a realizar analisis por cada variable
var = "id"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable id es 0. Lo que supone un 0.0% El nº de vacios para la variable id es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mrHWQAY | 1 | 0.008695 |
| 7013Y000001nEG3QAM | 1 | 0.008695 |
| 7013Y000001vaDMQAY | 1 | 0.008695 |
| 7013Y000001va6KQAQ | 1 | 0.008695 |
| 7013Y000001vaDbQAI | 1 | 0.008695 |
| ... | ... | ... |
| 7013Y0000011TdzQAE | 1 | 0.008695 |
| 7013Y000001mrhvQAA | 1 | 0.008695 |
| 7013Y000001mri3QAA | 1 | 0.008695 |
| 7013Y000001mriBQAQ | 1 | 0.008695 |
| 7013Y0000011VNkQAM | 1 | 0.008695 |
11501 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "id"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable id es 0. Lo que supone un 0.0% El nº de vacios para la variable id es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mrHWQAY | 1 | 0.008695 |
| 7013Y000001nEG3QAM | 1 | 0.008695 |
| 7013Y000001vaDMQAY | 1 | 0.008695 |
| 7013Y000001va6KQAQ | 1 | 0.008695 |
| 7013Y000001vaDbQAI | 1 | 0.008695 |
| ... | ... | ... |
| 7013Y0000011TdzQAE | 1 | 0.008695 |
| 7013Y000001mrhvQAA | 1 | 0.008695 |
| 7013Y000001mri3QAA | 1 | 0.008695 |
| 7013Y000001mriBQAQ | 1 | 0.008695 |
| 7013Y0000011VNkQAM | 1 | 0.008695 |
11501 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_attribute_1__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_1__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_attribute_1__c es 4816. Lo que supone un 41.8746195982958%
['msf_attribute_1__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 4816 | 41.874620 | |
| birthday | 1308 | 11.372924 |
| Camp trimestral | 549 | 4.773498 |
| Cumpleaños | 539 | 4.686549 |
| sports_event | 533 | 4.634380 |
| other | 454 | 3.947483 |
| cultural_event | 339 | 2.947570 |
| Otros | 252 | 2.191114 |
| F2F interno | 149 | 1.295540 |
| F2F externo | 125 | 1.086862 |
| Evento deportivo | 102 | 0.886879 |
| Google Search | 100 | 0.869490 |
| Mercadillo | 94 | 0.817320 |
| Newsletter | 94 | 0.817320 |
| Representación artística | 90 | 0.782541 |
| Encuentros solidarios | 77 | 0.669507 |
| 69 | 0.599948 | |
| El Pais | 48 | 0.417355 |
| Reto personal | 47 | 0.408660 |
| Regalo solidario | 42 | 0.365186 |
| Bajas | 35 | 0.304321 |
| Revista | 34 | 0.295626 |
| Afiliación Leads | 34 | 0.295626 |
| Tele5 | 33 | 0.286932 |
| TV (no sabe) | 30 | 0.260847 |
| corporate | 29 | 0.252152 |
| TVE-1 | 29 | 0.252152 |
| National Geographic | 28 | 0.243457 |
| Banner Web | 27 | 0.234762 |
| Evento solidario | 27 | 0.234762 |
| Antena3 | 26 | 0.226067 |
| Aniversario | 25 | 0.217372 |
| Venca | 25 | 0.217372 |
| anniversary | 25 | 0.217372 |
| La Vanguardia | 24 | 0.208678 |
| Giving tuesday | 23 | 0.199983 |
| in_memory_of | 23 | 0.199983 |
| El Correo | 23 | 0.199983 |
| Telefono encartes | 22 | 0.191288 |
| TVE-2 | 21 | 0.182593 |
| La voz de galicia | 19 | 0.165203 |
| Iniciativa de Empresa | 18 | 0.156508 |
| Cuatro | 18 | 0.156508 |
| Evento | 18 | 0.156508 |
| National Geographic historia | 17 | 0.147813 |
| El Periodico | 17 | 0.147813 |
| Mercadillo Solidario | 17 | 0.147813 |
| No sabe | 17 | 0.147813 |
| 902 deles | 16 | 0.139118 |
| El Mundo | 16 | 0.139118 |
| ABC | 16 | 0.139118 |
| Muy Interesante | 16 | 0.139118 |
| Diario de navarra | 15 | 0.130423 |
| TV-3 | 15 | 0.130423 |
| Evento Cultural | 15 | 0.130423 |
| National Geographic viajes | 15 | 0.130423 |
| TV delegaciones | 14 | 0.121729 |
| Heraldo de Aragón | 14 | 0.121729 |
| Concierto solidario | 13 | 0.113034 |
| Expositor bancos | 13 | 0.113034 |
| Diario vasco | 13 | 0.113034 |
| Faro de vigo-pontevedra | 12 | 0.104339 |
| BBVA | 12 | 0.104339 |
| En memoria de | 12 | 0.104339 |
| Diario de mallorca | 12 | 0.104339 |
| Sur | 12 | 0.104339 |
| Encarte bancos | 11 | 0.095644 |
| Levante | 11 | 0.095644 |
| La nueva españa-oviedo | 10 | 0.086949 |
| Publicidad gratuita | 10 | 0.086949 |
| Regala salud | 10 | 0.086949 |
| Impagos | 10 | 0.086949 |
| La provincia-canarias | 10 | 0.086949 |
| Google Display | 10 | 0.086949 |
| Diario de noticias (navarra) | 9 | 0.078254 |
| SMS 3a llamada | 9 | 0.078254 |
| D2D interno | 9 | 0.078254 |
| Las provincias | 9 | 0.078254 |
| La verdad | 9 | 0.078254 |
| Noticias de gipuzkoa | 8 | 0.069559 |
| Geo | 8 | 0.069559 |
| Diario de tarragona | 8 | 0.069559 |
| Diario montañes | 8 | 0.069559 |
| Diario de leon | 8 | 0.069559 |
| Diario de menorca | 8 | 0.069559 |
| Norte de castilla | 8 | 0.069559 |
| Deia noticias de bizkaia | 8 | 0.069559 |
| El comercio | 8 | 0.069559 |
| Hoy | 8 | 0.069559 |
| Diario de burgos | 7 | 0.060864 |
| Facebook messenger | 7 | 0.060864 |
| De viajes | 7 | 0.060864 |
| La opinion de tenerife | 7 | 0.060864 |
| Empresas | 7 | 0.060864 |
| Telemadrid | 7 | 0.060864 |
| Diario de ibiza | 7 | 0.060864 |
| Via digital | 7 | 0.060864 |
| La rioja | 7 | 0.060864 |
| Ideal | 7 | 0.060864 |
| Facebook Lead | 7 | 0.060864 |
| Saber vivir | 7 | 0.060864 |
| Exposición solidaria | 6 | 0.052169 |
| Bromera | 6 | 0.052169 |
| SMS 4a llamada | 6 | 0.052169 |
| Racc | 6 | 0.052169 |
| Pastillas contra dolor ajeno | 6 | 0.052169 |
| Invisibles en el pais | 6 | 0.052169 |
| 902 internet | 6 | 0.052169 |
| Clara | 6 | 0.052169 |
| Revista cottet | 6 | 0.052169 |
| Canal satelite digital | 6 | 0.052169 |
| Diario de noticias de alava | 6 | 0.052169 |
| Integral | 6 | 0.052169 |
| Audi magazine | 6 | 0.052169 |
| Informacion | 6 | 0.052169 |
| Encarte atrapados 2002 | 5 | 0.043474 |
| La opinion de zamora | 5 | 0.043474 |
| Pulsa | 5 | 0.043474 |
| Muy interesante historia | 5 | 0.043474 |
| Desnutrición | 5 | 0.043474 |
| Que leer | 5 | 0.043474 |
| La opinion de murcia | 5 | 0.043474 |
| Mutua scias | 5 | 0.043474 |
| SMS 2a llamada | 5 | 0.043474 |
| Prospectos | 5 | 0.043474 |
| SMS 5a llamada | 4 | 0.034780 |
| Google Youtube | 4 | 0.034780 |
| Manga films | 4 | 0.034780 |
| Diario de avila | 4 | 0.034780 |
| D2D externo | 4 | 0.034780 |
| Mi bebe y yo | 4 | 0.034780 |
| El jueves | 4 | 0.034780 |
| Sfera | 4 | 0.034780 |
| Haiti | 4 | 0.034780 |
| Emprendedores | 4 | 0.034780 |
| Reto deportivo | 4 | 0.034780 |
| Descobrir Catalunya | 4 | 0.034780 |
| Programática | 4 | 0.034780 |
| Infantil | 3 | 0.026085 |
| Autonomos | 3 | 0.026085 |
| Radio generico | 3 | 0.026085 |
| ADSALSA NO LLAMADOS ONG | 3 | 0.026085 |
| Diez minutos | 3 | 0.026085 |
| La opinion de a coruña | 3 | 0.026085 |
| XLSemanaL | 3 | 0.026085 |
| Comunishop movil | 3 | 0.026085 |
| Historia National Geographic | 3 | 0.026085 |
| Mia | 3 | 0.026085 |
| Revista ajuntament | 3 | 0.026085 |
| Delicatessen | 3 | 0.026085 |
| Webpilots | 3 | 0.026085 |
| Slider Web | 3 | 0.026085 |
| Clio | 3 | 0.026085 |
| La mañana de lerida | 3 | 0.026085 |
| Ana rosa | 3 | 0.026085 |
| 3 | 0.026085 | |
| Canarias 7 | 3 | 0.026085 |
| ABSERT | 3 | 0.026085 |
| Fotogramas | 3 | 0.026085 |
| DVD invisibles el pais | 3 | 0.026085 |
| AD735 | 3 | 0.026085 |
| Citibank | 3 | 0.026085 |
| Mundo oferta | 3 | 0.026085 |
| Tc Teléfono Fidelización | 2 | 0.017390 |
| Content ignition | 2 | 0.017390 |
| Comer y beber | 2 | 0.017390 |
| Tiktok | 2 | 0.017390 |
| La nueva españa | 2 | 0.017390 |
| Rutas del mundo | 2 | 0.017390 |
| Coregistro | 2 | 0.017390 |
| CENTRO NUTRICIONAL | 2 | 0.017390 |
| Viajes National Geographic | 2 | 0.017390 |
| Mediaset (T5+Cuatro) | 2 | 0.017390 |
| Abc (Nacional) | 2 | 0.017390 |
| El Periodico (Dominical) | 2 | 0.017390 |
| Mente sana | 2 | 0.017390 |
| Pc actual | 2 | 0.017390 |
| Diari de levante | 2 | 0.017390 |
| Ser padres hoy | 2 | 0.017390 |
| Ex circulo de lectores | 2 | 0.017390 |
| Diario avisos | 2 | 0.017390 |
| La Mañana De Lérida | 2 | 0.017390 |
| Diario de cadiz | 2 | 0.017390 |
| Historia y vida | 2 | 0.017390 |
| La sexta | 2 | 0.017390 |
| MEDIACOM/DIGITALCONTENT | 2 | 0.017390 |
| Banc Sabadell | 2 | 0.017390 |
| Mediterraneo | 2 | 0.017390 |
| Diario de jerez | 2 | 0.017390 |
| Avui | 2 | 0.017390 |
| Hoy badajoz | 2 | 0.017390 |
| Prensa iberica | 2 | 0.017390 |
| Planet 49 | 2 | 0.017390 |
| Ra cadena ser | 2 | 0.017390 |
| Lonely planet | 2 | 0.017390 |
| Espontáneos 902 250 902 | 2 | 0.017390 |
| Descobrir cuina | 2 | 0.017390 |
| Yemen | 2 | 0.017390 |
| Yate | 2 | 0.017390 |
| Revista lecturas | 2 | 0.017390 |
| Col medicos alicante | 2 | 0.017390 |
| Magazine - Mercedes Benz | 2 | 0.017390 |
| Recomendación conocidos | 2 | 0.017390 |
| Quo | 2 | 0.017390 |
| Sapiens | 2 | 0.017390 |
| Diario De León | 2 | 0.017390 |
| Tienda online | 2 | 0.017390 |
| Viajar | 2 | 0.017390 |
| 2 | 0.017390 | |
| Regalo de gran valor | 2 | 0.017390 |
| 1 | 0.008695 | |
| Mas alla | 1 | 0.008695 |
| Tc Teléfono Mailings | 1 | 0.008695 |
| El País (Eps) | 1 | 0.008695 |
| Cinco dias | 1 | 0.008695 |
| Bienvenida | 1 | 0.008695 |
| Colegios profesionales cantabria ju | 1 | 0.008695 |
| Tm Telefono web | 1 | 0.008695 |
| Diario Montañés | 1 | 0.008695 |
| Speak up | 1 | 0.008695 |
| Arquitectura y diseño | 1 | 0.008695 |
| Compractica movil | 1 | 0.008695 |
| Ultima hora | 1 | 0.008695 |
| Información | 1 | 0.008695 |
| Cupón Solicitado Por El Interesado | 1 | 0.008695 |
| Fundaciones | 1 | 0.008695 |
| El mueble | 1 | 0.008695 |
| Altair | 1 | 0.008695 |
| Muy Interesante-Historia | 1 | 0.008695 |
| Tc Teléfono Atención Al Socio | 1 | 0.008695 |
| Historia de iberia vieja | 1 | 0.008695 |
| Marie claire | 1 | 0.008695 |
| eldiario.es | 1 | 0.008695 |
| Mercedes benz | 1 | 0.008695 |
| Runners World | 1 | 0.008695 |
| Col med valencia | 1 | 0.008695 |
| Teléfono atención al Socio | 1 | 0.008695 |
| HERALDO DE ARAGON | 1 | 0.008695 |
| Nativa | 1 | 0.008695 |
| Car & driver | 1 | 0.008695 |
| El País (Eps) Cataluña | 1 | 0.008695 |
| Bing | 1 | 0.008695 |
| El Pais (Eps) Cataluña | 1 | 0.008695 |
| Internet cupon | 1 | 0.008695 |
| 13TV | 1 | 0.008695 |
| Col ats alava | 1 | 0.008695 |
| La Opinión De Tenerife | 1 | 0.008695 |
| Exsocios | 1 | 0.008695 |
| Barclays | 1 | 0.008695 |
| Herencias | 1 | 0.008695 |
| Muy interesante+geo | 1 | 0.008695 |
| Cupon mundo oferta | 1 | 0.008695 |
| Canal 33 | 1 | 0.008695 |
| Aleatorio score 0-0,5 tel | 1 | 0.008695 |
| Tc Teléfono Encartes | 1 | 0.008695 |
| Mailing | 1 | 0.008695 |
| El Dia Tenerife | 1 | 0.008695 |
| La opinion de malaga | 1 | 0.008695 |
| C.O. PLAZA MAYOR | 1 | 0.008695 |
| Soc no rec sin extra valor bajo | 1 | 0.008695 |
| El Mundo Catalunya | 1 | 0.008695 |
| Desconocido 902 250 902 | 1 | 0.008695 |
| Col med la rioja | 1 | 0.008695 |
| Tv Castilla La Mancha | 1 | 0.008695 |
| S no rec sin ext val b | 1 | 0.008695 |
| La Opinión De Murcia | 1 | 0.008695 |
| Tc Telefono Fidelizacion | 1 | 0.008695 |
| Caja abogados | 1 | 0.008695 |
| Donantes 1er año valor alto | 1 | 0.008695 |
| Cuerpo mente | 1 | 0.008695 |
| Crecer feliz | 1 | 0.008695 |
| CHARLA | 1 | 0.008695 |
| EITB | 1 | 0.008695 |
| Labores | 1 | 0.008695 |
| Clasicos exclusivos | 1 | 0.008695 |
| Profesionales | 1 | 0.008695 |
| Wc Clara | 1 | 0.008695 |
| Tc Telefono Mailings | 1 | 0.008695 |
| Cupon Solicitado Por El Interesado | 1 | 0.008695 |
| Tele 5 | 1 | 0.008695 |
| Interiores | 1 | 0.008695 |
| La redoute | 1 | 0.008695 |
| El Pais (Eps) | 1 | 0.008695 |
| Ara | 1 | 0.008695 |
| Publicación solidaria | 1 | 0.008695 |
| E-mailing angola junio 02 | 1 | 0.008695 |
| Beef! | 1 | 0.008695 |
| El Diario de Sevilla | 1 | 0.008695 |
| Cine Documental | 1 | 0.008695 |
| Llamada devos econ entropia | 1 | 0.008695 |
| Pastillas - doc farmacias | 1 | 0.008695 |
| Entidad financiera | 1 | 0.008695 |
| QUIENES SOMOS | 1 | 0.008695 |
| La Opinión De Málaga | 1 | 0.008695 |
| Diario De Ávila | 1 | 0.008695 |
| Europa sur | 1 | 0.008695 |
| socios con cambio cuota rec valor a | 1 | 0.008695 |
| Wc Beef! | 1 | 0.008695 |
| Internet | 1 | 0.008695 |
| El Dia De Soria | 1 | 0.008695 |
| Hea valencia | 1 | 0.008695 |
| CAPTACION EN LA CALLE | 1 | 0.008695 |
| Objetivo bienestar | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_attribute_2__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_2__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_attribute_2__c es 7264. Lo que supone un 63.15972524128337%
['msf_attribute_1__c', 'msf_attribute_2__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7264 | 63.159725 | |
| Genérico | 1083 | 9.416572 |
| Desnut | 796 | 6.921137 |
| Coronavirus | 734 | 6.382054 |
| FE | 339 | 2.947570 |
| Refugiados | 260 | 2.260673 |
| Camp trimestral octubre | 200 | 1.738979 |
| Camp trimestral fiscal | 121 | 1.052082 |
| Camp trimestral navidad | 116 | 1.008608 |
| Vacunación | 109 | 0.947744 |
| Camp trimestral junio | 87 | 0.756456 |
| Madrid | 34 | 0.295626 |
| Email (Lead) | 32 | 0.278237 |
| No Sabe | 31 | 0.269542 |
| Facebook (Lead) | 28 | 0.243457 |
| Camp trimestral | 25 | 0.217372 |
| Barcelona | 20 | 0.173898 |
| Bilbao | 15 | 0.130423 |
| Valencia | 12 | 0.104339 |
| Corregistro (Lead) | 11 | 0.095644 |
| Murcia | 10 | 0.086949 |
| Sevilla | 8 | 0.069559 |
| Málaga | 8 | 0.069559 |
| A Coruña | 7 | 0.060864 |
| Pamplona | 6 | 0.052169 |
| Mallorca | 6 | 0.052169 |
| Tenerife | 6 | 0.052169 |
| Santiago | 5 | 0.043474 |
| Retargeting | 5 | 0.043474 |
| Zaragoza | 5 | 0.043474 |
| Elche | 4 | 0.034780 |
| Lleida | 4 | 0.034780 |
| Jeréz | 4 | 0.034780 |
| Cádiz | 4 | 0.034780 |
| Siria | 4 | 0.034780 |
| San Sebastián | 4 | 0.034780 |
| Alicante | 4 | 0.034780 |
| Asturias | 4 | 0.034780 |
| Gran Canaria | 4 | 0.034780 |
| Vigo | 4 | 0.034780 |
| Extremadura | 3 | 0.026085 |
| Donosti | 3 | 0.026085 |
| Santander | 3 | 0.026085 |
| Salamanca | 3 | 0.026085 |
| Granada | 3 | 0.026085 |
| Toledo | 3 | 0.026085 |
| Segovia | 3 | 0.026085 |
| Córdoba | 3 | 0.026085 |
| La Palma | 3 | 0.026085 |
| Zamora | 3 | 0.026085 |
| Benicassim | 2 | 0.017390 |
| Ibiza | 2 | 0.017390 |
| Girona | 2 | 0.017390 |
| Tenerife Sur | 2 | 0.017390 |
| Puerto de Sta María | 2 | 0.017390 |
| Menorca | 2 | 0.017390 |
| Burgos | 2 | 0.017390 |
| Encuesta (Lead) | 2 | 0.017390 |
| Tossa De Mar | 2 | 0.017390 |
| Ciudad Real | 2 | 0.017390 |
| Valladolid | 2 | 0.017390 |
| Huesca | 1 | 0.008695 |
| Palencia | 1 | 0.008695 |
| Almería | 1 | 0.008695 |
| Lanzarote | 1 | 0.008695 |
| Redes Sociales (Lead) | 1 | 0.008695 |
| Pymes Madrid | 1 | 0.008695 |
| Fuerteventura | 1 | 0.008695 |
| Huelva | 1 | 0.008695 |
| Giving Tuesday | 1 | 0.008695 |
| Ceuta | 1 | 0.008695 |
| Logroño | 1 | 0.008695 |
| Tarragona | 1 | 0.008695 |
| Cuenca | 1 | 0.008695 |
| Display (Lead) | 1 | 0.008695 |
| Vitoria | 1 | 0.008695 |
| Melilla | 1 | 0.008695 |
| Castellon | 1 | 0.008695 |
| Norte | 1 | 0.008695 |
| Leon | 1 | 0.008695 |
| MALAGA | 1 | 0.008695 |
| San Sebastian | 1 | 0.008695 |
| Colera | 1 | 0.008695 |
| Memoria | 1 | 0.008695 |
| Guadalajara | 1 | 0.008695 |
| Castellón | 1 | 0.008695 |
| Camp trimestral diciembre | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_attribute_3__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_3__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_attribute_3__c es 10652. Lo que supone un 92.61803321450309%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 10652 | 92.618033 | |
| Creadores Sin Fronteras | 258 | 2.243283 |
| Non-Branded | 146 | 1.269455 |
| Inbound | 142 | 1.234675 |
| Zona Centro | 45 | 0.391270 |
| No sabe | 30 | 0.260847 |
| Zona Catalunya | 30 | 0.260847 |
| Branded | 29 | 0.252152 |
| Zona País Vasco | 24 | 0.208678 |
| Zona Andalucía Occidental | 21 | 0.182593 |
| Zona Levante Sur | 17 | 0.147813 |
| Zona Canarias | 17 | 0.147813 |
| Zona Galicia | 16 | 0.139118 |
| Zona Levante Norte | 15 | 0.130423 |
| Zona Andalucía Oriental | 13 | 0.113034 |
| Zona Castilla Y Leon | 12 | 0.104339 |
| Zona Baleares | 10 | 0.086949 |
| Zona Navarra | 6 | 0.052169 |
| Zona Aragón | 5 | 0.043474 |
| Zona Extremadura | 4 | 0.034780 |
| Zona Asturias | 3 | 0.026085 |
| Zona Cantabria | 3 | 0.026085 |
| Referral | 1 | 0.008695 |
| Zona La Rioja | 1 | 0.008695 |
| Zona Pais Vasco | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_attribute_4__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_4__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_attribute_4__c es 10943. Lo que supone un 95.14824797843666%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c']
# Vamos a realizar analisis por cada variable
var = "msf_attribute_5__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_attribute_5__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_attribute_5__c es 11223. Lo que supone un 97.58281888531431%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c']
# Vamos a realizar analisis por cada variable
var = "msf_campaigndonationreporting__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_campaigndonationreporting__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_campaigndonationreporting__c es 42. Lo que supone un 0.36518563603164944%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 16-Captación off resto | 3920 | 34.083993 |
| 13-Iniciativa Solidaria Online | 3356 | 29.180071 |
| 12-Iniciativa Solidaria off line | 867 | 7.538475 |
| 32-Mailing fide | 494 | 4.295279 |
| 33-Emailing fide | 438 | 3.808364 |
| 34-Officers Mid plus | 379 | 3.295366 |
| 31-Telemarketing fide | 303 | 2.634554 |
| 11-Tlmk captación | 285 | 2.478045 |
| 15-Televisión | 261 | 2.269368 |
| 23-Digital Orgánico | 185 | 1.608556 |
| 53-Resto | 176 | 1.530302 |
| 22-Digital Publi | 173 | 1.504217 |
| 52-Desconocido off | 169 | 1.469437 |
| 21-Digital leads (email/tlmk) | 142 | 1.234675 |
| 41-Officers Grandes empresas | 90 | 0.782541 |
| 35-Tlmk Mid | 79 | 0.686897 |
| 42-Officers Grandes donantes | 66 | 0.573863 |
| 36-Fide resto | 51 | 0.443440 |
| 42 | 0.365186 | |
| 14-Celebraciones | 24 | 0.208678 |
| 43-Officers Grandes fundaciones | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_campaignentryreporting__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_campaignentryreporting__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_campaignentryreporting__c es 42. Lo que supone un 0.36518563603164944%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 18-Captación off resto | 8148 | 70.846013 |
| 31-Mailing revista memoria Fide | 494 | 4.295279 |
| 33-Emailing fide | 438 | 3.808364 |
| 34-Fide resto | 430 | 3.738805 |
| 41-Resto | 332 | 2.886706 |
| 32-Tlmk conversión | 304 | 2.643248 |
| 23-Digital Orgánico | 186 | 1.617251 |
| 22-Digital publi | 173 | 1.504217 |
| 19-Desconocido off | 169 | 1.469437 |
| 13-Tlmk frío | 163 | 1.417268 |
| 11-F2F interno | 162 | 1.408573 |
| 21-Digital leads (email/tlmk) | 142 | 1.234675 |
| 12-F2F externo | 125 | 1.086862 |
| 35-Tlmk reactivación bajas | 68 | 0.591253 |
| 42 | 0.365186 | |
| 14-Tlmk SMS DRTIV | 41 | 0.356491 |
| 16-Tlmk rellamada | 38 | 0.330406 |
| 17-Tlmk prospectos | 23 | 0.199983 |
| 15-Tlmk sms otros | 13 | 0.113034 |
| 36-Tlmk impagos | 10 | 0.086949 |
# Vamos a realizar analisis por cada variable
var = "msf_canalsalidaconcatenado__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_canalsalidaconcatenado__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_canalsalidaconcatenado__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Iniciativa Solidaria - | 4227 | 36.753326 |
| Publi Ext - | 2270 | 19.737414 |
| Encarte - | 892 | 7.755847 |
| TLMK - | 763 | 6.634206 |
| Mailing - | 707 | 6.147292 |
| Emailings - | 518 | 4.503956 |
| Officers - | 472 | 4.103991 |
| F2F - | 283 | 2.460656 |
| Televisión - | 263 | 2.286758 |
| Desconocido - | 208 | 1.808538 |
| Otros - | 172 | 1.495522 |
| - | 119 | 1.034693 |
| Prensa o cupón - | 117 | 1.017303 |
| Redes Sociales - | 89 | 0.773846 |
| Paid Search - | 85 | 0.739066 |
| Orgánico - | 60 | 0.521694 |
| Publicidad digital - | 46 | 0.399965 |
| Display - | 40 | 0.347796 |
| Afiliación - | 35 | 0.304321 |
| Celebraciones - | 24 | 0.208678 |
| Banners - | 18 | 0.156508 |
| Exposiciones - | 17 | 0.147813 |
| Email - | 16 | 0.139118 |
| Dipticos - | 16 | 0.139118 |
| D2D - | 13 | 0.113034 |
| SMS - | 12 | 0.104339 |
| Radio - | 11 | 0.095644 |
| Eventos - | 4 | 0.034780 |
| Mensajería Instantánea - | 3 | 0.026085 |
| Tienda MSF - | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_campaignentryreporting__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_campaignentryreporting__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_campaignentryreporting__c es 42. Lo que supone un 0.36518563603164944%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 18-Captación off resto | 8148 | 70.846013 |
| 31-Mailing revista memoria Fide | 494 | 4.295279 |
| 33-Emailing fide | 438 | 3.808364 |
| 34-Fide resto | 430 | 3.738805 |
| 41-Resto | 332 | 2.886706 |
| 32-Tlmk conversión | 304 | 2.643248 |
| 23-Digital Orgánico | 186 | 1.617251 |
| 22-Digital publi | 173 | 1.504217 |
| 19-Desconocido off | 169 | 1.469437 |
| 13-Tlmk frío | 163 | 1.417268 |
| 11-F2F interno | 162 | 1.408573 |
| 21-Digital leads (email/tlmk) | 142 | 1.234675 |
| 12-F2F externo | 125 | 1.086862 |
| 35-Tlmk reactivación bajas | 68 | 0.591253 |
| 42 | 0.365186 | |
| 14-Tlmk SMS DRTIV | 41 | 0.356491 |
| 16-Tlmk rellamada | 38 | 0.330406 |
| 17-Tlmk prospectos | 23 | 0.199983 |
| 15-Tlmk sms otros | 13 | 0.113034 |
| 36-Tlmk impagos | 10 | 0.086949 |
# Vamos a realizar analisis por cada variable
var = "msf_isemergency__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_isemergency__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isemergency__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| False | 11138 | 96.843753 |
| True | 363 | 3.156247 |
# Vamos a realizar analisis por cada variable
var = "msf_isonline__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_isonline__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_isonline__c es 139. Lo que supone un 1.2085905573428397%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| No | 6891 | 59.916529 |
| Si | 4457 | 38.753152 |
| 139 | 1.208591 | |
| NA | 14 | 0.121729 |
# Vamos a realizar analisis por cada variable
var = "msf_objective__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_objective__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_objective__c es 105. Lo que supone un 0.9129640900791236%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Captación de socios o donantes | 6618 | 57.542822 |
| Captación de leads | 2342 | 20.363447 |
| Upgrade | 1129 | 9.816538 |
| Cultivación | 494 | 4.295279 |
| Desconocido | 263 | 2.286758 |
| Conversión | 215 | 1.869403 |
| Conversión (de lead o donante a socio) | 215 | 1.869403 |
| 105 | 0.912964 | |
| Recuperación | 85 | 0.739066 |
| Otros | 23 | 0.199983 |
| Petición difusión | 4 | 0.034780 |
| Informativo | 3 | 0.026085 |
| Rendición de cuentas | 3 | 0.026085 |
| Fidelización | 1 | 0.008695 |
| Captación | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_objectivepublic__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_objectivepublic__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_objectivepublic__c es 10272. Lo que supone un 89.31397269802626%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_objectivepublic__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 10272 | 89.313973 | |
| MASS | 1229 | 10.686027 |
# Vamos a realizar analisis por cada variable
var = "msf_outboundchannel1__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_outboundchannel1__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_outboundchannel1__c es 119. Lo que supone un 1.0346926354230066%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Iniciativa Solidaria | 4227 | 36.753326 |
| Publi Ext | 2270 | 19.737414 |
| Encarte | 892 | 7.755847 |
| TLMK | 763 | 6.634206 |
| Mailing | 707 | 6.147292 |
| Emailings | 518 | 4.503956 |
| Officers | 472 | 4.103991 |
| F2F | 283 | 2.460656 |
| Televisión | 263 | 2.286758 |
| Desconocido | 208 | 1.808538 |
| Otros | 172 | 1.495522 |
| 119 | 1.034693 | |
| Prensa o cupón | 117 | 1.017303 |
| Redes Sociales | 89 | 0.773846 |
| Paid Search | 85 | 0.739066 |
| Orgánico | 60 | 0.521694 |
| Publicidad digital | 46 | 0.399965 |
| Display | 40 | 0.347796 |
| Afiliación | 35 | 0.304321 |
| Celebraciones | 24 | 0.208678 |
| Banners | 18 | 0.156508 |
| Exposiciones | 17 | 0.147813 |
| 16 | 0.139118 | |
| Dipticos | 16 | 0.139118 |
| D2D | 13 | 0.113034 |
| SMS | 12 | 0.104339 |
| Radio | 11 | 0.095644 |
| Eventos | 4 | 0.034780 |
| Mensajería Instantánea | 3 | 0.026085 |
| Tienda MSF | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_outboundchannel2__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_outboundchannel2__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_outboundchannel2__c es 11501. Lo que supone un 100.0%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_objectivepublic__c', 'msf_outboundchannel2__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 11501 | 100.0 |
# Vamos a realizar analisis por cada variable
var = "msf_ownby__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_ownby__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_ownby__c es 133. Lo que supone un 1.1564211807668898%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Captación | 8713 | 75.758630 |
| Fidelización | 1744 | 15.163899 |
| Digital | 474 | 4.121381 |
| Desconocido | 185 | 1.608556 |
| Colaboraciones Estratégicas | 157 | 1.365099 |
| 133 | 1.156421 | |
| Otros | 95 | 0.826015 |
# Vamos a realizar analisis por cada variable
var = "msf_previousstepchannel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_previousstepchannel__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_previousstepchannel__c es 11187. Lo que supone un 97.26980262585863%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_objectivepublic__c', 'msf_outboundchannel2__c', 'msf_previousstepchannel__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 11187 | 97.269803 | |
| Lead online | 166 | 1.443353 |
| SMS TV | 43 | 0.373881 |
| TLMK | 42 | 0.365186 |
| One to One | 31 | 0.269542 |
| 14 | 0.121729 | |
| SMS Opis | 10 | 0.086949 |
| SMS Push | 5 | 0.043474 |
| SMS otros | 3 | 0.026085 |
# Vamos a realizar analisis por cada variable
var = "msf_promoterindividual__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_promoterindividual__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_promoterindividual__c es 7475. Lo que supone un 64.99434831753761%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_objectivepublic__c', 'msf_outboundchannel2__c', 'msf_previousstepchannel__c', 'msf_promoterindividual__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7475 | 64.994348 | |
| 0033Y00002us2UrQAI | 28 | 0.243457 |
| 0033Y00002up1OsQAI | 23 | 0.199983 |
| 0033Y00003LdoAXQAZ | 21 | 0.182593 |
| 0033Y00002zXqidQAC | 16 | 0.139118 |
| ... | ... | ... |
| 0033Y00003Oy0wlQAB | 1 | 0.008695 |
| 0033Y00002unZbrQAE | 1 | 0.008695 |
| 0033Y00002uokfgQAA | 1 | 0.008695 |
| 0033Y00002up3jeQAA | 1 | 0.008695 |
| 0033Y00003j0QGYQA2 | 1 | 0.008695 |
3410 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_provider__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_provider__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_provider__c es 7279. Lo que supone un 63.29014868272325%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_objectivepublic__c', 'msf_outboundchannel2__c', 'msf_previousstepchannel__c', 'msf_promoterindividual__c', 'msf_provider__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7279 | 63.290149 | |
| Betternow | 3310 | 28.780106 |
| Datem | 355 | 3.086688 |
| Taskphone | 231 | 2.008521 |
| Desconocido | 61 | 0.530389 |
| Consolidar | 46 | 0.399965 |
| T2O | 43 | 0.373881 |
| Twisters | 35 | 0.304321 |
| Taskforce | 33 | 0.286932 |
| Entropia | 14 | 0.121729 |
| Prosocial | 12 | 0.104339 |
| Bodasnet | 11 | 0.095644 |
| Sitel | 11 | 0.095644 |
| Google Ads | 11 | 0.095644 |
| Zankyou | 10 | 0.086949 |
| FISL | 7 | 0.060864 |
| Paypal | 6 | 0.052169 |
| Fundraisingco | 6 | 0.052169 |
| 5 | 0.043474 | |
| INEK | 4 | 0.034780 |
| Centrocom | 2 | 0.017390 |
| Vodafone | 1 | 0.008695 |
| Worldcoo | 1 | 0.008695 |
| Movistar | 1 | 0.008695 |
| DGTL | 1 | 0.008695 |
| Pluscontacto | 1 | 0.008695 |
| Inneria | 1 | 0.008695 |
| Testamenta | 1 | 0.008695 |
| Iberian | 1 | 0.008695 |
| Busquets | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_segment__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_segment__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_segment__c es 110. Lo que supone un 0.9564385705590819%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Frio individuos | 5635 | 48.995740 |
| Frío individuos | 3329 | 28.945309 |
| Mass donors | 1183 | 10.286062 |
| Mid donors | 363 | 3.156247 |
| Leads | 280 | 2.434571 |
| Organizaciones | 145 | 1.260760 |
| Mid+ donors | 142 | 1.234675 |
| Desconocido | 125 | 1.086862 |
| 110 | 0.956439 | |
| Testamentarios | 87 | 0.756456 |
| One to One | 66 | 0.573863 |
| Asociados España | 10 | 0.086949 |
| Asociados | 9 | 0.078254 |
| Celebraciones | 7 | 0.060864 |
| Otros | 6 | 0.052169 |
| Terreno | 4 | 0.034780 |
# Vamos a realizar analisis por cada variable
var = "recordtypeid"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable recordtypeid es 0. Lo que supone un 0.0% El nº de vacios para la variable recordtypeid es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0120O000000kNMGQA2 | 11459 | 99.634814 |
| 0123Y000000ZVFyQAO | 42 | 0.365186 |
# Vamos a realizar analisis por cada variable
var = "status"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable status es 0. Lo que supone un 0.0% El nº de vacios para la variable status es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Completed | 8780 | 76.341188 |
| In Progress | 1494 | 12.990175 |
| Created | 1207 | 10.494740 |
| Canceled | 20 | 0.173898 |
# Vamos a realizar analisis por cada variable
var = "ownerid"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable ownerid es 0. Lo que supone un 0.0% El nº de vacios para la variable ownerid es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0050O000009jTv8QAE | 6257 | 54.403965 |
| 0050O0000092PPIQA2 | 2080 | 18.085384 |
| 0050O000009jOwLQAU | 1077 | 9.364403 |
| 0053Y00000AHjdNQAT | 396 | 3.443179 |
| 0053Y00000AHjd3QAD | 395 | 3.434484 |
| 0050O0000073qRgQAI | 285 | 2.478045 |
| 0053Y00000AHKLsQAP | 214 | 1.860708 |
| 0053Y000009bDUuQAM | 206 | 1.791149 |
| 0053Y0000096s8QQAQ | 144 | 1.252065 |
| 0053Y00000A6ntvQAB | 100 | 0.869490 |
| 0053Y00000AKTiDQAX | 57 | 0.495609 |
| 0053Y00000AVogPQAT | 56 | 0.486914 |
| 0050O000009jVBvQAM | 49 | 0.426050 |
| 0053Y0000096yrMQAQ | 42 | 0.365186 |
| 0053Y00000A6arDQAR | 26 | 0.226067 |
| 0050O000007DgJhQAK | 25 | 0.217372 |
| 0053Y000008IZePQAW | 15 | 0.130423 |
| 0053Y00000A6YtHQAV | 13 | 0.113034 |
| 0053Y0000096yKrQAI | 13 | 0.113034 |
| 0053Y0000096yr7QAA | 10 | 0.086949 |
| 0053Y00000AHjdhQAD | 9 | 0.078254 |
| 0053Y00000AHXaHQAX | 6 | 0.052169 |
| 0053Y00000AHQCYQA5 | 5 | 0.043474 |
| 0050O0000073qbgQAA | 4 | 0.034780 |
| 0050O000007DgJcQAK | 4 | 0.034780 |
| 0050O0000073qbWQAQ | 3 | 0.026085 |
| 0053Y00000AHQCbQAP | 3 | 0.026085 |
| 0053Y00000A6b9CQAR | 3 | 0.026085 |
| 0053Y00000A6cgCQAR | 2 | 0.017390 |
| 0053Y00000BR4oJQAT | 1 | 0.008695 |
| 0053Y00000AHjceQAD | 1 | 0.008695 |
# Vamos a realizar analisis por cada variable
var = "msf_thematic__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_thematic__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_thematic__c es 111. Lo que supone un 0.9651334666550734%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 90 | 6128 | 53.282323 |
| 34 | 801 | 6.964612 |
| 81 | 791 | 6.877663 |
| 00 | 519 | 4.512651 |
| 33 | 348 | 3.025824 |
| 74 | 274 | 2.382402 |
| 04 | 212 | 1.843318 |
| 28 | 149 | 1.295540 |
| 10 | 142 | 1.234675 |
| 06 | 133 | 1.156421 |
| 66 | 122 | 1.060777 |
| 05 | 121 | 1.052082 |
| 07 | 113 | 0.982523 |
| 111 | 0.965133 | |
| 65 | 87 | 0.756456 |
| 08 | 87 | 0.756456 |
| 39 | 85 | 0.739066 |
| 03 | 83 | 0.721676 |
| 71 | 81 | 0.704287 |
| 52 | 77 | 0.669507 |
| 31 | 76 | 0.660812 |
| 99 | 64 | 0.556473 |
| 53 | 52 | 0.452135 |
| 85 | 52 | 0.452135 |
| 70 | 51 | 0.443440 |
| 50 | 48 | 0.417355 |
| 02 | 47 | 0.408660 |
| 11 | 43 | 0.373881 |
| 36 | 41 | 0.356491 |
| 37 | 34 | 0.295626 |
| 18 | 31 | 0.269542 |
| 42 | 30 | 0.260847 |
| 12 | 30 | 0.260847 |
| 80 | 26 | 0.226067 |
| 60 | 26 | 0.226067 |
| 86 | 23 | 0.199983 |
| 54 | 22 | 0.191288 |
| 72 | 19 | 0.165203 |
| 91 | 18 | 0.156508 |
| 43 | 18 | 0.156508 |
| Asamblea General | 17 | 0.147813 |
| 40 | 16 | 0.139118 |
| MSF España | 15 | 0.130423 |
| 64 | 14 | 0.121729 |
| 69 | 14 | 0.121729 |
| 32 | 12 | 0.104339 |
| 59 | 12 | 0.104339 |
| 17 | 11 | 0.095644 |
| 83 - Afganistán | 11 | 0.095644 |
| 57 | 10 | 0.086949 |
| 67 | 9 | 0.078254 |
| 82-Tigray | 9 | 0.078254 |
| 30 | 8 | 0.069559 |
| 63 | 8 | 0.069559 |
| 68 | 7 | 0.060864 |
| 27 | 7 | 0.060864 |
| 49 | 7 | 0.060864 |
| 61 | 6 | 0.052169 |
| Vida asociativa | 6 | 0.052169 |
| 38 | 6 | 0.052169 |
| 76 | 6 | 0.052169 |
| 55 | 6 | 0.052169 |
| 25 | 5 | 0.043474 |
| 77 | 5 | 0.043474 |
| 13 | 5 | 0.043474 |
| 15 | 4 | 0.034780 |
| Gobernanza | 4 | 0.034780 |
| 78 | 4 | 0.034780 |
| 87 | 4 | 0.034780 |
| 84 | 3 | 0.026085 |
| 45 | 3 | 0.026085 |
| 46 | 3 | 0.026085 |
| 22 | 3 | 0.026085 |
| 41 | 2 | 0.017390 |
| 51 | 2 | 0.017390 |
| 24 | 2 | 0.017390 |
| 62 | 2 | 0.017390 |
| 20 | 2 | 0.017390 |
| 48 | 2 | 0.017390 |
| 56 | 2 | 0.017390 |
| 23 | 2 | 0.017390 |
| 88 | 1 | 0.008695 |
| 89 | 1 | 0.008695 |
| 21 | 1 | 0.008695 |
| 58 | 1 | 0.008695 |
| XX | 1 | 0.008695 |
| 35 | 1 | 0.008695 |
| 29 | 1 | 0.008695 |
| 75 | 1 | 0.008695 |
| 44 | 1 | 0.008695 |
| 16 | 1 | 0.008695 |
# Vamos a analizar la tabla Campañas
df = df_tareas
# Se crea una lista por ahora vacia, en la que se irán añadiendo las variables que se van a eliminar del dataset por motivos varios: no utilidad, gran volumen de nulos, ...
col_to_delete_tareas=list()
# Vamos a realizar analisis por cada variable
var = "msf_Objective__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_Objective__c es 0. Lo que supone un 0.0% El nº de vacios para la variable msf_Objective__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Petición económica-Upgrade Socio | 2611895 | 99.995827 |
| Petición económica-Conversión Prospecto | 107 | 0.004096 |
| Gestión administrativa | 1 | 0.000038 |
| Petición económica-Resto | 1 | 0.000038 |
# Vamos a realizar analisis por cada variable
var = "msf_CloseType__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_CloseType__c es 62202. Lo que supone un 2.381389921301805% El nº de vacios para la variable msf_CloseType__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| No util | 958125 | 37.576447 |
| Negativo | 840737 | 32.972639 |
| Positivo | 574342 | 22.524965 |
| Descargada | 91922 | 3.605064 |
| No útil | 55934 | 2.193661 |
| Potencial | 28742 | 1.127225 |
# Vamos a realizar analisis por cada variable
var = "ActivityDate"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable ActivityDate es 0. Lo que supone un 0.0% El nº de vacios para la variable ActivityDate es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2016-10-28 | 44842 | 1.716766 |
| 2015-05-17 | 32781 | 1.255013 |
| 2023-05-26 | 32705 | 1.252104 |
| 2013-10-11 | 23392 | 0.895558 |
| 2018-09-30 | 22995 | 0.880359 |
| ... | ... | ... |
| 2015-05-16 | 1 | 0.000038 |
| 2014-10-26 | 1 | 0.000038 |
| 2013-10-13 | 1 | 0.000038 |
| 2022-07-02 | 1 | 0.000038 |
| 2023-07-02 | 1 | 0.000038 |
2748 rows × 2 columns
# Vamos a realizar analisis por cada variable
var = "msf_Channel__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_Channel__c es 1. Lo que supone un 3.828478057460861e-05% El nº de vacios para la variable msf_Channel__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Llamada | 2513077 | 96.212638 |
| 98920 | 3.787132 | |
| Interno | 2 | 0.000077 |
| Mensajería Instantánea | 2 | 0.000077 |
| Fichero Informático | 1 | 0.000038 |
| Correo Postal | 1 | 0.000038 |
# Vamos a realizar analisis por cada variable
var = "msf_Campaign__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_Campaign__c es 210116. Lo que supone un 8.044244955214463% El nº de vacios para la variable msf_Campaign__c es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 7013Y000001mqtMQAQ | 1176702 | 48.990711 |
| 7013Y000001vXGdQAM | 224728 | 9.356306 |
| 7013Y000001n865QAA | 138421 | 5.763008 |
| 7013Y000001vXGiQAM | 110245 | 4.589931 |
| 7013Y000001n860QAA | 104716 | 4.359737 |
| 7013Y000001DrxyQAC | 96915 | 4.034951 |
| 7013Y000001vZqHQAU | 88236 | 3.673610 |
| 7013Y000001DrxoQAC | 67535 | 2.811746 |
| 7013Y000001mrgZQAQ | 62087 | 2.584925 |
| 7013Y000001myedQAA | 51595 | 2.148102 |
| 7013Y000001mqtuQAA | 51081 | 2.126702 |
| 7013Y000001mrgeQAA | 36976 | 1.539456 |
| 7013Y000001mrgcQAA | 34374 | 1.431124 |
| 7013Y000001myefQAA | 27801 | 1.157464 |
| 7013Y000001najdQAA | 23703 | 0.986849 |
| 7013Y000001vZoBQAU | 23536 | 0.979896 |
| 7013Y000001myeaQAA | 20745 | 0.863696 |
| 7013Y000001n4QNQAY | 10643 | 0.443110 |
| 7013Y000000kQ7rQAE | 9863 | 0.410635 |
| 7013Y000001njxrQAA | 7806 | 0.324994 |
| 7013Y000001mrgaQAA | 5163 | 0.214956 |
| 7013Y000001mrjZQAQ | 3894 | 0.162122 |
| 7013Y000001mrjVQAQ | 3372 | 0.140390 |
| 7013Y000001mrgXQAQ | 2806 | 0.116825 |
| 7013Y000001myebQAA | 2299 | 0.095716 |
| 7013Y000001n4QWQAY | 2125 | 0.088472 |
| 7013Y000001mqt6QAA | 2117 | 0.088139 |
| 7013Y000001mrgbQAA | 1830 | 0.076190 |
| 7013Y000001mrgdQAA | 1488 | 0.061951 |
| 7013Y000001mrjXQAQ | 1392 | 0.057954 |
| 7013Y000001mrgYQAQ | 1298 | 0.054041 |
| 7013Y000001mrgfQAA | 836 | 0.034806 |
| 7013Y000001mrjlQAA | 824 | 0.034306 |
| 7013Y000001vZoGQAU | 814 | 0.033890 |
| 7013Y000001myecQAA | 802 | 0.033390 |
| 7013Y000001myeYQAQ | 655 | 0.027270 |
| 7013Y000001myeeQAA | 367 | 0.015280 |
| 7013Y000001myeZQAQ | 313 | 0.013031 |
| 7013Y000001mrjkQAA | 302 | 0.012573 |
| 7013Y000001mrjnQAA | 252 | 0.010492 |
| 7013Y000001myegQAA | 241 | 0.010034 |
| 7013Y000001mrjmQAA | 216 | 0.008993 |
| 7013Y000001mrjoQAA | 134 | 0.005579 |
| 7013Y000001vCMxQAM | 105 | 0.004372 |
| 7013Y000001mrjfQAA | 100 | 0.004163 |
| 7013Y000001mN9IQAU | 92 | 0.003830 |
| 7013Y000001mN9DQAU | 92 | 0.003830 |
| 7013Y000001mrjpQAA | 78 | 0.003247 |
| 7013Y000001mqrjQAA | 73 | 0.003039 |
| 7013Y000001mqt7QAA | 63 | 0.002623 |
| 7013Y000001mrjqQAA | 22 | 0.000916 |
| 7013Y000001mquNQAQ | 11 | 0.000458 |
| 7013Y000001mrZeQAI | 2 | 0.000083 |
| 7013Y000001mrjWQAQ | 1 | 0.000042 |
| 7013Y000001mN98QAE | 1 | 0.000042 |
# Vamos a realizar analisis por cada variable
var = "msf_StartDate__c"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable msf_StartDate__c es 1526897. Lo que supone un 58.45691660502818% El nº de vacios para la variable msf_StartDate__c es 0. Lo que supone un 0.0%
['msf_attribute_1__c', 'msf_attribute_2__c', 'msf_attribute_3__c', 'msf_attribute_4__c', 'msf_attribute_5__c', 'msf_objectivepublic__c', 'msf_outboundchannel2__c', 'msf_previousstepchannel__c', 'msf_promoterindividual__c', 'msf_provider__c', 'msf_StartDate__c']
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 2020-10-08 | 91649 | 8.446080 |
| 2023-05-26 | 47000 | 4.331370 |
| 2022-12-05 | 42875 | 3.951223 |
| 2022-10-19 | 40000 | 3.686272 |
| 2020-10-05 | 29023 | 2.674667 |
| 2020-11-27 | 28449 | 2.621769 |
| 2021-06-01 | 27102 | 2.497634 |
| 2020-11-02 | 25697 | 2.368154 |
| 2023-03-03 | 23593 | 2.174256 |
| 2023-02-03 | 23504 | 2.166054 |
| 2023-03-29 | 23500 | 2.165685 |
| 2023-06-27 | 23500 | 2.165685 |
| 2023-04-28 | 23450 | 2.161077 |
| 2022-10-18 | 23107 | 2.129467 |
| 2021-11-09 | 22884 | 2.108916 |
| 2021-02-01 | 22001 | 2.027542 |
| 2022-11-07 | 21648 | 1.995011 |
| 2022-04-06 | 21600 | 1.990587 |
| 2022-10-06 | 21600 | 1.990587 |
| 2022-06-08 | 21600 | 1.990587 |
| 2022-09-07 | 21600 | 1.990587 |
| 2022-01-10 | 21600 | 1.990587 |
| 2022-08-03 | 21600 | 1.990587 |
| 2022-07-07 | 21600 | 1.990587 |
| 2022-03-10 | 21600 | 1.990587 |
| 2022-05-10 | 21600 | 1.990587 |
| 2022-02-10 | 21473 | 1.978883 |
| 2021-09-07 | 19968 | 1.840187 |
| 2021-07-01 | 19846 | 1.828944 |
| 2021-03-01 | 18965 | 1.747754 |
| 2021-12-02 | 17720 | 1.633019 |
| 2021-10-05 | 17386 | 1.602238 |
| 2023-01-09 | 16300 | 1.502156 |
| 2021-09-28 | 13785 | 1.270382 |
| 2021-08-03 | 13312 | 1.226791 |
| 2020-11-06 | 12765 | 1.176382 |
| 2021-10-04 | 11672 | 1.075654 |
| 2021-05-03 | 11582 | 1.067360 |
| 2021-09-08 | 11032 | 1.016674 |
| 2023-04-19 | 10375 | 0.956127 |
| 2021-04-07 | 9374 | 0.863878 |
| 2021-04-06 | 9311 | 0.858072 |
| 2021-10-28 | 9205 | 0.848303 |
| 2023-01-04 | 8967 | 0.826370 |
| 2021-10-26 | 7806 | 0.719376 |
| 2023-02-15 | 6075 | 0.559853 |
| 2023-07-07 | 5932 | 0.546674 |
| 2022-11-25 | 5177 | 0.477096 |
| 2020-12-29 | 4938 | 0.455070 |
| 2022-09-08 | 4758 | 0.438482 |
| 2022-12-28 | 4581 | 0.422170 |
| 2023-02-01 | 4516 | 0.416180 |
| 2023-07-05 | 4314 | 0.397564 |
| 2021-05-04 | 3851 | 0.354896 |
| 2023-06-28 | 3834 | 0.353329 |
| 2022-12-24 | 3270 | 0.301353 |
| 2022-11-09 | 3229 | 0.297574 |
| 2021-01-15 | 2900 | 0.267255 |
| 2023-02-22 | 2812 | 0.259145 |
| 2023-01-17 | 2724 | 0.251035 |
| 2022-12-08 | 2682 | 0.247165 |
| 2023-01-11 | 2632 | 0.242557 |
| 2022-11-30 | 2443 | 0.225139 |
| 2023-03-09 | 2046 | 0.188553 |
| 2023-05-03 | 2024 | 0.186525 |
| 2023-03-14 | 1818 | 0.167541 |
| 2023-04-26 | 1268 | 0.116855 |
| 2022-09-22 | 1120 | 0.103216 |
| 2023-01-26 | 1067 | 0.098331 |
| 2023-02-09 | 1042 | 0.096027 |
| 2023-07-04 | 1000 | 0.092157 |
| 2023-05-10 | 992 | 0.091420 |
| 2023-06-30 | 992 | 0.091420 |
| 2023-06-06 | 962 | 0.088655 |
| 2022-12-14 | 903 | 0.083218 |
| 2022-07-27 | 814 | 0.075016 |
| 2023-03-02 | 779 | 0.071790 |
| 2020-12-30 | 604 | 0.055663 |
| 2022-09-09 | 365 | 0.033637 |
| 2023-03-16 | 317 | 0.029214 |
| 2022-11-28 | 192 | 0.017694 |
| 2020-12-17 | 184 | 0.016957 |
| 2023-06-14 | 176 | 0.016220 |
| 2023-05-18 | 161 | 0.014837 |
| 2023-06-21 | 101 | 0.009308 |
| 2022-12-22 | 97 | 0.008939 |
| 2022-11-19 | 95 | 0.008755 |
| 2023-05-16 | 83 | 0.007649 |
| 2023-01-12 | 83 | 0.007649 |
| 2023-06-16 | 70 | 0.006451 |
| 2023-02-10 | 68 | 0.006267 |
| 2022-10-27 | 67 | 0.006175 |
| 2022-12-01 | 64 | 0.005898 |
| 2023-01-25 | 56 | 0.005161 |
| 2022-11-10 | 52 | 0.004792 |
| 2023-04-27 | 50 | 0.004608 |
| 2022-10-21 | 49 | 0.004516 |
| 2023-01-19 | 43 | 0.003963 |
| 2022-10-12 | 41 | 0.003778 |
| 2022-10-05 | 41 | 0.003778 |
| 2022-12-30 | 35 | 0.003225 |
| 2022-11-02 | 30 | 0.002765 |
| 2022-09-28 | 28 | 0.002580 |
| 2022-12-29 | 17 | 0.001567 |
| 2022-12-25 | 15 | 0.001382 |
| 2023-02-25 | 11 | 0.001014 |
| 2020-11-20 | 10 | 0.000922 |
| 2023-02-24 | 9 | 0.000829 |
| 2022-12-10 | 9 | 0.000829 |
| 2022-11-17 | 6 | 0.000553 |
| 2023-05-04 | 6 | 0.000553 |
| 2023-02-23 | 6 | 0.000553 |
| 2023-01-31 | 5 | 0.000461 |
| 2023-03-01 | 5 | 0.000461 |
| 2020-09-30 | 5 | 0.000461 |
| 2023-05-17 | 4 | 0.000369 |
| 2023-03-11 | 4 | 0.000369 |
| 2023-01-13 | 4 | 0.000369 |
| 2022-11-03 | 4 | 0.000369 |
| 2023-02-16 | 3 | 0.000276 |
| 2023-03-07 | 3 | 0.000276 |
| 2023-02-26 | 3 | 0.000276 |
| 2023-05-07 | 3 | 0.000276 |
| 2023-03-10 | 3 | 0.000276 |
| 2020-11-05 | 3 | 0.000276 |
| 2020-10-30 | 3 | 0.000276 |
| 2023-01-27 | 3 | 0.000276 |
| 2023-01-07 | 3 | 0.000276 |
| 2020-10-16 | 3 | 0.000276 |
| 2023-02-21 | 2 | 0.000184 |
| 2023-02-02 | 2 | 0.000184 |
| 2023-04-20 | 2 | 0.000184 |
| 2023-05-12 | 2 | 0.000184 |
| 2023-02-12 | 2 | 0.000184 |
| 2022-12-15 | 2 | 0.000184 |
| 2022-12-17 | 2 | 0.000184 |
| 2023-07-03 | 2 | 0.000184 |
| 2020-09-22 | 2 | 0.000184 |
| 2022-12-03 | 2 | 0.000184 |
| 2022-12-06 | 2 | 0.000184 |
| 2023-01-14 | 2 | 0.000184 |
| 2022-07-13 | 2 | 0.000184 |
| 2023-05-30 | 2 | 0.000184 |
| 2021-04-28 | 2 | 0.000184 |
| 2023-05-08 | 1 | 0.000092 |
| 2023-04-25 | 1 | 0.000092 |
| 2023-01-21 | 1 | 0.000092 |
| 2023-04-30 | 1 | 0.000092 |
| 2023-05-28 | 1 | 0.000092 |
| 2023-05-02 | 1 | 0.000092 |
| 2023-04-24 | 1 | 0.000092 |
| 2023-05-13 | 1 | 0.000092 |
| 2021-04-01 | 1 | 0.000092 |
| 2023-05-19 | 1 | 0.000092 |
| 2022-07-06 | 1 | 0.000092 |
| 2022-07-05 | 1 | 0.000092 |
| 2023-05-11 | 1 | 0.000092 |
| 2022-06-22 | 1 | 0.000092 |
| 2023-05-20 | 1 | 0.000092 |
| 2023-04-21 | 1 | 0.000092 |
| 2021-02-17 | 1 | 0.000092 |
| 2023-01-16 | 1 | 0.000092 |
| 2021-03-08 | 1 | 0.000092 |
| 2023-02-18 | 1 | 0.000092 |
| 2021-11-04 | 1 | 0.000092 |
| 2021-06-15 | 1 | 0.000092 |
| 2023-01-06 | 1 | 0.000092 |
| 2021-11-05 | 1 | 0.000092 |
| 2022-03-02 | 1 | 0.000092 |
| 2021-03-24 | 1 | 0.000092 |
| 2021-05-07 | 1 | 0.000092 |
| 2021-11-11 | 1 | 0.000092 |
| 2021-11-12 | 1 | 0.000092 |
| 2021-09-24 | 1 | 0.000092 |
| 2020-09-23 | 1 | 0.000092 |
| 2021-01-07 | 1 | 0.000092 |
| 2020-11-03 | 1 | 0.000092 |
| 2020-11-25 | 1 | 0.000092 |
| 2021-02-12 | 1 | 0.000092 |
| 2020-10-28 | 1 | 0.000092 |
| 2023-01-10 | 1 | 0.000092 |
| 2020-09-24 | 1 | 0.000092 |
| 2023-01-20 | 1 | 0.000092 |
| 2022-11-18 | 1 | 0.000092 |
| 2022-01-05 | 1 | 0.000092 |
| 2021-09-01 | 1 | 0.000092 |
| 2022-12-16 | 1 | 0.000092 |
| 2023-02-11 | 1 | 0.000092 |
| 2023-02-28 | 1 | 0.000092 |
| 2021-08-24 | 1 | 0.000092 |
| 2021-06-17 | 1 | 0.000092 |
| 2022-12-09 | 1 | 0.000092 |
| 2023-04-12 | 1 | 0.000092 |
| 2023-01-05 | 1 | 0.000092 |
| 2023-01-01 | 1 | 0.000092 |
| 2022-11-22 | 1 | 0.000092 |
| 2023-01-15 | 1 | 0.000092 |
| 2023-07-02 | 1 | 0.000092 |
# Vamos a realizar analisis por cada variable
var = "Status"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable Status es 0. Lo que supone un 0.0% El nº de vacios para la variable Status es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| Realizada | 2493233 | 95.452878 |
| Cancelada | 78406 | 3.001757 |
| En curso | 40358 | 1.545097 |
| Pendiente | 7 | 0.000268 |
# Vamos a realizar analisis por cada variable
var = "WhoId"
# Analizamos nulos
count_nulos(df,var,col_to_delete_campaign)
El nº de nulos para la variable WhoId es 0. Lo que supone un 0.0% El nº de vacios para la variable WhoId es 0. Lo que supone un 0.0%
# Analizamos posibles valores de la variable
freq_variables(df,var)
| # Tot | % Tot | |
|---|---|---|
| 0033Y00002uNnhXQAS | 20 | 0.000766 |
| 0033Y00002uNx9eQAC | 19 | 0.000727 |
| 0033Y00002unVk3QAE | 18 | 0.000689 |
| 0033Y00002upRCVQA2 | 18 | 0.000689 |
| 0033Y00002uNtqnQAC | 18 | 0.000689 |
| ... | ... | ... |
| 0033Y00002uoIGUQA2 | 1 | 0.000038 |
| 0033Y00002uo6BbQAI | 1 | 0.000038 |
| 0033Y00002unwzPQAQ | 1 | 0.000038 |
| 0033Y00002uo2o9QAA | 1 | 0.000038 |
| 0033Y00003CCkqXQAT | 1 | 0.000038 |
593604 rows × 2 columns